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  • The oestrous cycle stage affects mammary tumour sensitivity to chemotherapy

    The oestrous cycle stage affects mammary tumour sensitivity to chemotherapy

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    Patient samples

    All retrospective medical data/biospecimen studies at the Netherlands Cancer Institute have been executed pursuant to Dutch legislation and international standards. Before 25 May 2018, national legislation on data protection applied, as well as the International Guideline on Good Clinical Practice. From 25 May 2018 we also adhered to the General Data Protection Regulation of the European Union. Within this framework, patients are informed and have always had the opportunity to object or actively consent to the (continued) use of their personal data and biospecimens in research. Hence, the procedures comply both with (inter)national legislative and ethical standards. All retrospective medical data/biospecimens provided by LUMC were part of the DIRECT study (NCT02126449 (ref. 45)), were conducted in accordance with the Declaration of Helsinki (October 2013) and were approved by the Ethics Committee of LUMC in agreement with Dutch law for medical research involving human subjects.

    Assessment of HER2 status in this study followed established guidelines, with scoring based on both immunohistochemistry (IHC) and in situ hybridization techniques. Immunohistochemistry results were categorized as 0/1+ (HER2-negative), 2+ (equivocal; considered positive if amplification was detected with in situ hybridization) or 3+ (HER2-positive). Assessment of oestrogen receptor was performed according to Dutch guidelines, with scores of 10% or above considered positive, and those below 10% negative. Pathological complete response (pCR) scoring adhered to local standard guidelines. For one patient, pCR was determined based on radiological images because surgery was not performed due to pCR having been achieved. For measurement of tumour reduction from baseline, we compared pretreatment radiological images with post-treatment residual disease as assessed by pathological evaluation.

    Experimental model and subject data

    All mice were adult females, housed under a 12/12 h light/dark cycle and under specific-pathogen-free laboratory conditions and received food and water ad libitum. All experiments were approved and performed according to the guidelines of the Animal Welfare Committees of the Royal Dutch Academy for Sciences (Hubrecht Institute), the Netherlands Cancer Institute or KU Leuven. Sample size was not determined a priori. Mice were randomly assigned to experimental groups. All experiments were performed in a blinded manner, except for tumour volume measurement, which was performed by the same investigator who administered chemotherapy treatment, making it impossible to work in a blinded manner.

    MMTV-PyMT26 and MMTV-Cre61 mice were purchased from Jackson Laboratory. E-cad-mCFP mice62 were a gift from H. Clevers, R26-loxP-stop-loxP-YFP (R26R-YFP) mice a gift from J. Deschamps and MMTV-Wnt1 (ref. 30) mice a gift from J. Hilkens. MMTV-PyMT;R26R-Confetti;R26-CreERT2 mice62,63, of a mixed BL6/FVB genetic background, were used to label and trace single cells by IVM. Following tumour development, mice were intraperitoneally injected with tamoxifen (Sigma-Aldrich; 1.5 mg 25 g−1, diluted in sunflower oil) to activate Cre recombinase and induce colour randomization of the confetti cassette. MMTV–Wnt1;R26R-Confetti:R26-CreERT2 mice62,63, of a mixed BL6/FVB genetic background, were used to isolate tumour pieces, which were transplanted into NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) female mice (8–12 weeks of age) Following tumour development, mice were intraperitoneally injected with tamoxifen (Sigma-Aldrich; 1.5 mg 25 g−1, diluted in sunflower oil) to activate Cre recombinase and induce colour randomization of the confetti cassette. Staining and treatment experiments in transgenic mice were performed on MMTV-PyMT;MMTV-Cre;R26-LSL-YFP;E-cad-mCFP and MMTV-Wnt1;MMTV-Cre; R26-LSL-YFP;E-cad-mCFP mice of a pure FVB genetic background.

    For staining and treatment experiments in transplanted models, tumours from three independent, treatment-naive MMTV-PyMT;MMTV-Cre;R26-LSL-YFP;E-cad-mCFP donors were harvested. Mammary tumours were minced and enzymatically digested by gentle shaking for 30 min at 37 C in digestion mix (0.2% trypsin from bovine pancreas; Sigma) and 0.2% collagenase A (Roche)). The digested tumours were spun down and cell fragments embedded in basal membrane extract (BME) (RGF BME type 2 PathClear). Mammary tumour organoid medium contained DMEM/F12 GlutaMAX (GIBCO), 2% B27 (Invitrogen) and 10 ng ml−1 fibroblast growth factor. Brca1−/−Trp53−/− organoids were a gift from J. Jonkers. Both MMTV-PyMT and Brca1−/−Trp53−/− organoids were orthotopically transplanted into both fourth mammary glands of 8–12-week-old FVB/NRj female mice (Janvier Labs).

    For transplantation of MMTV-PyMT organoids, 250,000 single cells were plated 3 days before transplantation. On the day of transplantation, BME was dissolved by mechanical disruption and cells dissolved in 100 μl of BME type 2 (RGF BME type 2 PathClear)/PBS. For transplantation of Brca1−/−Trp53−/− organoids, 10.000 single cells were transplanted in both fourth mammary glands of FVB/NRj mice.

    Staging of mice

    For determination of the oestrous stage of mice, a vaginal smear was collected and analysed as described in refs. 24,46,64. In short, the vagina was flushed with 50 µl of PBS which was then transferred to a glass slide. Following air drying, the slide was stained with 0.01% crystal violet and oestrus stage determined by examination of cytological characteristics using a light microscope. Mice were categorized as regularly cycling when they showed cycles of length 4–6 days37.

    Ovariectomy

    In a subgroup of mice, ovariectomy was performed when mammary tumours of volume 50 mm3 could be palpated, after which mice were allowed to recover for at least 7 days before IVM or treatment.

    Chemotherapeutic treatment of mice

    When tumours had reached a cumulative volume of 500–750 mm3 following short-term treatment, or either 250–450 mm3 (transgenic MMTV–PyMT model) or 50–200 mm3 (transplanted models) for long-term treatment, mice were treated with either vehicle (PBS) or chemotherapeutic—either doxorubicin hydrochloride (5 mg kg−1; catalogue no. D1515, Sigma-Aldrich) or cyclophosphamide monohydrate (100 mg kg−1; catalogue no. C7397, Sigma-Aldrich)—once per week by intravenous injection. Treatment cycles were continued for a maximum of 5 weeks or until the humane endpoint was reached (cumulative tumour load over 750 mm3 or single tumour over 350 mm3). Tumour diameter was measured three times per week by calipers. Following the last treatment cycle, primary tumours and lungs were collected from these mice for further analyses.

    Depletion experiments

    When mice approached a cumulative tumour volume of 500–750 mm3, they began receiving depletion antibodies at least 1 week before chemotherapeutic treatment and this continued until the end of the experiment. Individual mice were treated every week with either 60 mg kg−1 of either InVivoMAb anti-mouse CSF1R (CD115) monoclonal antibody (BioXCell, clone AFS98, catalogue no. BE0213) or InVivoMAb rat IgG2a isotype control (BioXCell, clone 2A3, catalogue no. BE0089), or received InVivoMAb anti-mouse CD4 (BioXCell, clone GK1.5, catalogue no. BE003-1) and InVivoMAb anti-mouse CD8 (BioXCell, clone YTS 169.4, catalogue no. BP0117), three times per week, every other day, with a first dose of 400 µg followed by 200 µg per antibody.

    Mammary imaging window implantation and IVM

    To follow in vivo tumour clone dynamics, a mammary imaging window was inserted into tumour-bearing mice 2–3 days following induction of lineage tracing. Mice were anaesthetized using a mixture of 2% isoflurane and compressed air, with surgery performed under aseptic conditions. Before surgery, the skin overlying the tumour was shaved and the skin disinfected using 70% ethanol. An incision was made through the skin overlying the tumour and an imaging window inserted65,66. The imaging window was secured using a non-absorbable, non-woven, purse‐string suture (4‐0 prolene suture) in the skin. For imaging, mice were sedated using isoflurane inhalation anaesthesia (roughly 2.0% isoflurane/compressed air mixture) and received 200 µl of sterile PBS by subcutaneous injection to prevent dehydration. Mice were placed in a custom-designed imaging box on the microscope stage and maintained under constant anaesthesia under the microscope within a temperature-adjusted climate chamber (34.5 °C). Following each imaging session, mice were allowed to recover on a heat mat. Imaging was performed on either an inverted Leica TCS SP5 AOBS multiphoton microscope with a chameleon Ti:Sapphire pumped Optical Parametric Oscillator (Coherent Inc.; www.coherent.com) or a Leica SP8-DIVE confocal microscope equipped with an Insight X3 dual-beam pulsed layer (Spectra Physics; www.spectra-physics.com), equipped with non-descanned detectors. CFP was excited at a wavelength of 840 nm, and yellow fluorescent protein (YFP), green flourescent protein and red flourescent protein at a wavelength of 960 nm. All images were in 12-bit and acquired with a ×25 (HCX IRAPO, numerical aperture 0.95, working distance 2.5 mm) water objective with a free working distance of 2.40 mm. Three-dimensional tile scans of large tumour areas were taken at the indicated time points (at least 5.0 × 5.0 × 0.5 mm3 (x, y, z)) with z-steps of 5–10 µm. Tile scans were collected at regular intervals over time periods of up to 5 weeks following induction of lineage tracing. The same imaging fields were followed during subsequent imaging sessions, using the imaging coordinates of the first imaging session.

    Postprocessing and analysis of IVM data

    For analysis of three-dimensional tile scans, all intravital images were stitched and processed using Leica Application Suite X software (Leica Microsystems) and ImageJ software (https://imagej.net). Clones were manually annotated in the images, and clone size (number of cells) counted in three dimensions throughout the z-stacks. By following the same clones in our intravital images, we quantified individual clone sizes over time. The fraction of clones that behaved in a synchronized manner with phases of growth, when clones become larger, followed by a phase of regression during which the clones become smaller, was denoted as alternating. To distinguish synchronized clones from non-synchronized in quantitative terms, we identified as synchronized clones those that showed clear, regular alternating phases of growth and regression with the same periodicity, similar to a sinusoidal wave, over multiple consecutive cycles throughout the imaging sessions. By contrast, non-synchronized clones did not exhibit this regular pattern.

    Histochemistry and immunohistopathology

    Mouse tissues were formalin fixed and paraffin embedded. Haematoxylin and eosin staining was performed on 2 µm sections and IHC staining on 4 µm sections using routine procedures. For IHC staining, antigen retrieval was performed with Tris/EDTA (pH 9.0) (Tris: Sigma, catalogue no. 252859; EDTA: Sigma, catalogue no. EDS) for ER-α (Invitrogen, clone 6F11, catalogue no. MA1-27107, 1:100), CD4 (Invitrogen, clone 4SM95, catalogue no. 14-9766-82, 1:1,000), CD8 (Invitrogen, clone 4SM15, catalogue no. 14-0808-82, 1:2,000), CD31 (Cell Signaling, catalogue no. 77699S, 1:100), F4/80 (Cell Signaling, catalogue no. 70076S, 1:1,000) or citrate buffer (pH 6.0) for progesterone receptor (Fisher Scientific, clone SP2, catalogue no. RM-9102-S1, 1:300). Sections were incubated with primary antibodies overnight at 4 °C. For CD4 and CD8 staining, additional labelling was performed by incubation of goat anti-rat biotin (SouthernBiotech, catalogue no. 3052-08, 1:150) antibody for 30 min at room temperature. Following washing, binding of primary antibody was visualized dependent on species using either the EnVision+ HRP Labelled Polymer anti-rabbit/mouse system (Dako, catalogue nos. K400, K4003 and K4001) or Streptavidin/HRP (Dako, catalogue no. P0397) and the Liquid DAB+ Substrate Chromogen System (Dako, catalogue no. K3468); counterstaining with haaematoxylin was then performed. Slides were digitally processed using a PANNORAMIC 1000 whole-slide scanner (3DHISTECH) and images captured with SlideViewer 2.7 (3DHISTECH). For determination of positive cells, IHC staining was quantified using QuPath 0.4.4 (GitHub) with atomized classifiers.

    Immunostaining

    Tumour samples were fixed in periodate/lysine/4% paraformaldehyde buffer overnight at 4 °C, incubated in 30% sucrose overnight at 4 °C and embedded in Tissue Freezing medium (Leica Biosystems). Tumours were cryosectioned and immunostaining performed on 10 µm sections. For this, sections were hydrated in PBS for 10 min at room temperature and subsequently blocked and permeabilized for 1 h with 0.5% TritonX/5% normal goat serum (NGS) in PBS. The primary antibody used was anti-PHH3 (Millipore, catalogue no. 06-570, 1:500); the antibody was diluted in 0.1% TritonX/5% NGS in PBS, and tissues were sectioned and stained overnight at 4 °C. Following three washes in PBS, the secondary antibody (donkey anti-rabbit AF568; Invitrogen, catalogue no. A10042, 1:1,000) was incubated for 1 h at room temperature and nuclei stained with TO-PRO-3 (Invitrogen, catalogue no. T3605, 1:5,000). Following three 10 min washes in PBS, stained sections were mounted using VectaShield. All stainings were imaged with an inverted Leica TCS SP8 confocal microscope. Fluorophores were excited as follows: AF594 at 561 nm, YFP at 514 nm and TO-PRO-3 at 633 nm. YFP was collected at 519–555 nm, Alexa-568 at 575–630 nm and TO-PRO-3 at 644–694 nm. All images were collected in 12-bit with a ×25 water-immersion objective (HC FLUOTAR L, numerical aperture 0.95, working distance 2.4 mm). For determination of positive cells, staining was quantified using Fiji/ImageJ v.1.49k and Excel 2016.

    EdU detection

    EdU was injected intraperitoneally 4 h before mice were killed (1 mg per animal, 5 mg ml−1 stock in PBS; Sigma, catalogue no. 900584). EdU staining was performed on paraffin sections of thickness 10 µm; sections were deparaffinized, and hot target retrieval performed in citrate buffer pH 6.0. EdU was detected by incubation with 100 mM Tris pH 8.5, 1 mM CuSO4, 100 mM ascorbic acid and 10 μM AlexaFluor-488 azide (Invitrogen, catalogue no. A10266) for 30 min at room temperature. Subsequently, DNA counterstaining was performed with TO-PRO-3 (Invitrogen, catalogue no. T3605, 1:5,000) in 0.1% TritonX/5% NGS in PBS for 1 h at room temperature. Following three 10 min washes in PBS, stained sections were mounted using VectaShield. All staining was imaged with an inverted Leica TCS SP8 confocal microscopes. Fluorophores were excited as follows: AF488 at 488 nm and TO-PRO-3 at 633 nm; AF488 was collected at 492–530 nm and TO-PRO-3 at 650–700 nm. All images were collected in 12-bit with a ×25 water-immersion objective (HC FLUOTAR L numerical aperture 0.95 W, VISIR 0.17, FWD 2.4 mm). For determination of positive cells, staining was quantified using semiautomated macros in Fiji/ImageJ v.1.49k and Excel 2016.

    Flow-cytometric analysis of dying cells

    Mammary tumours were collected separately and minced on ice using sterile scalpels, followed by digestion for depletion experiments in 25 μg ml−1 DNase I (Roche) and 5 Wünsch units TH Liberase ml−1 (Roche) in PBS at 37 C for 35 min, or in 25 μg ml−1 DNase I (Roche) and 3 mg ml−1 collagenase A (Sigma) in PBS at 37 °C for 1 h. The digestion mix was filtered through a 70 µm filter (BD Falcon) while adding DMEM/F12 + GlutaMAX, followed by spinning down for 4 min at 500 relative centrifugal force at 4 °C and resuspension of pellets in 5 mM EDTA/PBS. Cells were washed once in 5 mM EDTA/PBS and centrifuged (4 min at 500 relative centrifugal force at room temperature) before proceeding with antibody labelling. Tumour cells were blocked in fluorescent activated cell-sorting buffer supplied with 20% normal goat serum (Gibco) for 10 min on ice, before labelling with one of the following antibody combinations for depletion experiments: (1) E-cad-eFluor660 (catalogue no. DECMA-1, eBioscience, 1:200), biotin-conjugated anti-mouse CD41 clone eBioMWReg30 (eBioscience, catalogue no. 13-0411-821, 1:200) and anti-mouse CD45 clone 30-F11 (eBioscience, catalogue no. 13-0451-85, 1:200); (2) E-cad-eFluor660 (catalogue no. DECMA-1, eBioscience, 1:200); and (3) anti-mouse CD45-BUV395 clone 30-F11 (BD Bioscience, catalogue no. 564279, 1:200). Secondary labelling was performed using streptavidin-conjugated PerCP (BioLegend, catalogue no. 405213, 1:200). Dead cells were stained using the Apoptosis detection kit PE (Invitrogen, catalogue no. 88-8102-74) according to the manufacturer’s protocol. For depletion experiments no secondary labelling was performed and, following washing, cells were immediately stained with PI and analysed on a Symphony A5 (BD Biosciences) then washed once in 5 nM EDTA/PBS and analysed on a Symphony A5 (BD Biosciences). A broad forward scatter/side scatter (FSC/SSC) gate was followed by gates excluding doublets. Immune cells and megakaryocytes were then excluded, based on staining for CD41 and CD45, in a dump channel. Tumour cells were selected according to YFP positivity and further stringently gated for the presence of the cell death marker annexin and PI, or for PI only for depletion experiments. Data were manually analysed with FlowJo v.10.6.2. Analysis of staining and fluorescent activated cell-sorted samples was pseudomized by number coding.

    RNA isolation, complementary DNA preparation and qPCR

    RNA was isolated using Trizol reagent (Invitrogen Life Technologies) according to the manufacturer’s protocol. Purity and amount of RNA isolated were analysed using a Nanodrop spectrophotometer. Complementary DNA was prepared using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems) according to the manufacturer’s protocol. Sequences of used primers can be found below. Quantitative PCR (qPCR) was performed using Power SYBR Green PCR Master Mix (Applied Biosystems). Thermal cycle conditions used for all qPCR reactions were as follows: 5 min at 95 °C, followed by 40 cycles of denaturation for 30 s at 95 °C, annealing for 30 s at 60 °C and extension for 1 min at 72 °C. PCR reactions were concluded with incubation for 10 min at 72 °C to complete the extension of all synthesized products.

    Progesterone assay

    To 250 µl serum or calibrator samples was added 10 µl of deuterated internal standard (progesterone-d9, CDN Isotopes). Subsequently, progesterone was extracted with 1 ml of methyl tert-butyl ether and dried using a SpeedVac concentrator. Dried samples were reconstituted in 100 µl of injection solution (methanol:water 2:3, v:v). Thereafter, samples were centrifuged for 5.0 min at 18,213g and 50 µl was injected into a Nexera SIL30ACMP (Shimadzu) autosampler. Chromatographic separation was achieved using a Kinetex EVO 1.7 µm C18 column (2.1 mm, internal diameter 50 mm) (Phenomenex). A gradient protocol of two mobile phases, containing water with 0.1% formic acid and 2 mM ammonium acetate, and methanol, was applied at a flow rate of 0.6 ml min−1. The gradient started at 50% methanol, which was gradually increased to 75% over 2.3 min. Next, the column was washed with 100% methanol for 0.5 min before returning to the starting condition of 50% methanol, for 0.7 min. The assay had a total run time of 3.5 min. Tandem mass spectrometry analysis was performed with a QTRAP6500+ instrument (Sciex) operated in positive electrospray ionization mode (600 °C) and multiple-reaction monitoring mode. For quantitation of progesterone, mass:charge ratio, 315.1 → 97.0 (progesterone) and 324.2 → 100.0 (progesterone-d9) were monitored. The assay is standardized against the NIST SRM 971 standard. In two serum pools containing 1.1 and 27.5 nmol l−1, total coefficient of variation was 9.5 and 7.5%, respectively. The lower limit of quantitation was determined at 0.96 nmol l−1 (coefficient of variation 8.3%) in a serum pool containing low progesterone levels. Analysis of patient samples (sera) was performed blinded, and linked to tumour volume measurements and other clinical parameters only at a later stage.

    Statistics and reproducibility

    Statistical analyses were performed using R v.4.4.2 (R Development Core Team and the R Foundation for Statistical Computing) by integration of software from open-source packages. For further details on statistics see Supplementary File 1. The level of statistical significance was set at #P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001. For all violin plots, thicker solid centre lines represent the median, thinner solid lines the 25th and 75th percentiles. Statistical analyses of longitudinal mouse data were performed using R v.4.4.2 (R Development Core Team and the R Foundation for Statistical Computing) by integrating software from open-source packages, including nlme67, JMbayes2 (ref. 68) and packages from tidyverse69, including dplyr, tidyr and ggplot2, and were analysed as follows. Both tumour volume over time and time to event (either end of study or death) were recorded for each individual animal. To test for survival, survival regression (Cox) models were used to test whether survival probability between groups was different. For longitudinal tumour measurements, the mixed-effects model70 was used. Regression splines (natural cubic splines with 2 or 3 degrees of freedom, depending on the case) were used to fit a nonlinear tumour growth function per animal along time, taking into account all separate tumours for each individual animal separately. This model takes the correlated structure of the data into consideration by inclusion of individual animals as a grouping factor, and tumour (within the animal) as a subgrouping factor. Furthermore, the effects of time and treatment were considered as random and fixed effects, respectively. A joint model was used to combine survival and tumour volume measurements, to determine whether the responses together were different between treatment groups. Splines were again used to represent the effect of time. For this model, multiple tumours per animal had their growth curves averaged. For further details on these statistics, see Supplementary File 2.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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  • Early versus deferred use of CDK4/6 inhibitors in advanced breast cancer

    Early versus deferred use of CDK4/6 inhibitors in advanced breast cancer

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  • Finn, R. S. et al. Palbociclib and letrozole in advanced breast cancer. N. Engl. J. Med. 375, 1925–1936 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Hortobagyi, G. N. et al. Updated results from MONALEESA-2, a phase III trial of first-line ribociclib plus letrozole versus placebo plus letrozole in hormone receptor-positive, HER2-negative advanced breast cancer. Ann. Oncol. 29, 1541–1547 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Johnston, S. et al. MONARCH 3 final PFS: a randomized study of abemaciclib as initial therapy for advanced breast cancer. NPJ Breast Cancer 5, 5 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Tripathy, D. et al. Ribociclib plus endocrine therapy for premenopausal women with hormone-receptor-positive, advanced breast cancer (MONALEESA-7): a randomised phase 3 trial. Lancet Oncol. 19, 904–915 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Cristofanilli, M. et al. Fulvestrant plus palbociclib versus fulvestrant plus placebo for treatment of hormone-receptor-positive, HER2-negative metastatic breast cancer that progressed on previous endocrine therapy (PALOMA-3): final analysis of the multicentre, double-blind, phase 3 randomised controlled trial. Lancet Oncol. 17, 425–439 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Slamon, D. J. et al. Phase III randomized study of ribociclib and fulvestrant in hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer: MONALEESA-3. J. Clin. Oncol. 36, 2465–2472 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Sledge, G. W. et al. MONARCH 2: abemaciclib in combination with fulvestrant in women with HR+/HER2− advanced breast cancer who had progressed while receiving endocrine therapy. J. Clin. Oncol. 35, 2875–2884 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • van Ommen-Nijhof, A. et al. Selecting the optimal position of CDK4/6 inhibitors in hormone receptor-positive advanced breast cancer—the SONIA study: study protocol for a randomized controlled trial. BMC Cancer 18, 1146 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Sledge, G. W. et al. The effect of abemaciclib plus fulvestrant on overall survival in hormone receptor-positive, ERBB2-negative breast cancer that progressed on endocrine therapy-MONARCH 2: a randomized clinical trial. JAMA Oncol. 6, 116–124 (2020).

    Article 
    PubMed 

    Google Scholar     

  • Hortobagyi, G. N. et al. Overall survival with ribociclib plus letrozole in advanced breast cancer. N. Engl. J. Med. 386, 942–950 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Slamon, D. J. et al. Overall survival with ribociclib plus fulvestrant in advanced breast cancer. N. Engl. J. Med. 382, 514–524 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Lu, Y. S. et al. Updated overall survival of ribociclib plus endocrine therapy versus endocrine therapy alone in pre- and perimenopausal patients with HR+/HER2− advanced breast cancer in MONALEESA-7: a phase III randomized clinical trial. Clin. Cancer Res. 28, 851–859 (2022).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Gradishar, W. J. et al. NCCN Guidelines Insights: Breast cancer, Version 4.2023. J. Natl Compr. Cancer Netw.21, 594–608 (2023).

  • Kümler, I., Knoop, A. S., Jessing, C. A., Ejlertsen, B. & Nielsen, D. L. Review of hormone-based treatments in postmenopausal patients with advanced breast cancer focusing on aromatase inhibitors and fulvestrant. ESMO Open 1, e000062 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Yang, C. et al. Acquired CDK6 amplification promotes breast cancer resistance to CDK4/6 inhibitors and loss of ER signaling and dependence. Oncogene 36, 2255–2264 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Park, Y. H. et al. Longitudinal multi-omics study of palbociclib resistance in HR-positive/HER2-negative metastatic breast cancer. Genome Med. 15, 55 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Gyawali, B. et al. Problematic crossovers in cancer drug trials. Nat. Rev. Clin. Oncol. 20, 815–816 (2023).

    Article 
    PubMed 

    Google Scholar     

  • Spring, L. M. et al. Cyclin-dependent kinase 4 and 6 inhibitors for hormone receptor-positive breast cancer: past, present, and future. Lancet 395, 817–827 (2020). A.

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Cherny, N. I. et al. A standardised, generic, validated approach to stratify the magnitude of clinical benefit that can be anticipated from anti-cancer therapies: the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS). Ann. Oncol. 26, 1547–1573 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • G-standaard, Tarieven Januari (Z-index, 2023).

  • G-standaard, Tarieven Januari (Z-index, 2019).

  • Centers for Medicare & Medicaid Services. Medicare Part D Spending by Drug (Centers for Medicare & Medicaid Services, accessed 8 May 2024); data.cms.gov/summary-statistics-on-use-and-payments/medicare-medicaid-spending-by-drug/medicaid-spending-by-drug.

  • Johnston, S. et al. Abemaciclib as initial therapy for advanced breast cancer: MONARCH 3 updated results in prognostic subgroups. NPJ Breast Cancer 7, 80 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Rugo, H. S. et al. Palbociclib plus letrozole as first-line therapy in estrogen receptor-positive/human epidermal growth factor receptor 2-negative advanced breast cancer with extended follow-up. Breast Cancer Res. Treat. 174, 719–729 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Di Lauro, V. et al. Health-related quality of life in breast cancer patients treated with CDK4/6 inhibitors: a systematic review. ESMO Open 7, 100629 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Turner, N. C. et al. Overall survival with palbociclib and fulvestrant in advanced breast cancer. N. Engl. J. Med. 379, 1926–1936 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Finn, S. R. et al. Overall survival (OS) with first-line palbociclib plus letrozole (PAL+LET) versus placebo plus letrozole (PBO+LET) in women with estrogen receptor–positive/human epidermal growth factor receptor 2–negative advanced breast cancer (ER+/HER2−ABC): Analyses from PALOMA-2. J. Clin. Oncol. 40, LBA1003 (2022).

    Article 

    Google Scholar     

  • André, F. et al. Alpelisib for PIK3CA-mutated, hormone receptor-positive advanced breast cancer. N. Engl. J. Med. 380, 1929–1940 (2019).

    Article 
    PubMed 

    Google Scholar     

  • Turner, N. C. et al. Capivasertib in hormone receptor-positive advanced breast cancer. N. Engl. J. Med. 388, 2058–2070 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Bidard, F. C. et al. Elacestrant (oral selective estrogen receptor degrader) versus standard endocrine therapy for estrogen receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer: results from the randomized phase III EMERALD trial. J. Clin. Oncol. 40, 3246–3256 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Garcia-Fructuoso, I., Gomez-Bravo, R. & Schettini, F. Integrating new oral selective oestrogen receptor degraders in the breast cancer treatment. Curr. Opin. Oncol. 34, 635–642 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Woodford, R. et al. Validity and efficiency of progression-free survival-2 as a surrogate end point for overall survival in advanced cancer randomized trials. JCO Precis. Oncol. 8, e2300296 (2024).

    Article 
    PubMed 

    Google Scholar     

  • European Medicines Agency. Appendix 1 to the Guideline on the Evaluation of Anticancer Medicinal Products in Man (EMA, 2012).

  • Fojo, T. & Simon, R. M. Inappropriate censoring in Kaplan-Meier analyses. Lancet Oncol. 22, 1358–1360 (2021).

    Article 
    PubMed 

    Google Scholar     

  • Gyawali, B. et al. Biases in study design, implementation, and data analysis that distort the appraisal of clinical benefit and ESMO-Magnitude of Clinical Benefit Scale (ESMO-MCBS) scoring. ESMO Open 6, 100117 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Committee for Medicinal Products for Human Use. Guideline on the Choice of the Non-Inferiority Margin (EMA, 2005).

  • Tannock, I. F. et al. The tyranny of non-inferiority trials. Lancet Oncol. 25, e520–e525 (2024).

  • Committee for Medicinal Products for Human Use. Concept Paper for the Development of a Guideline on Non-Inferiority and Equivalence Comparisons in Clinical Trials (EMA, 2024).

  • André, F. et al. Alpelisib plus fulvestrant for PIK3CA-mutated, hormone receptor-positive, human epidermal growth factor receptor-2-negative advanced breast cancer: final overall survival results from SOLAR-1. Ann. Oncol. 32, 208–217 (2021).

    Article 
    PubMed 

    Google Scholar     

  • Robson, M. E. et al. OlympiAD final overall survival and tolerability results: Olaparib versus chemotherapy treatment of physician’s choice in patients with a germline BRCA mutation and HER2-negative metastatic breast cancer. Ann. Oncol. 30, 558–566 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Casparie, M. et al. Pathology databanking and biobanking in The Netherlands, a central role for PALGA, the nationwide histopathology and cytopathology data network and archive. Cell Oncol. 29, 19–24 (2007).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Cancer Therapy Evaluation Program. Common Terminology Criteria for Adverse Events (CTCAE) v.4.0 (National Cancer Institute, 2010).

  • Brady, M. J. et al. Reliability and validity of the functional assessment of cancer therapy-breast quality-of-life instrument. J. Clin. Oncol. 15, 974–986 (1997).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Eton, D. T. et al. A combination of distribution- and anchor-based approaches determined minimally important differences (MIDs) for four endpoints in a breast cancer scale. J. Clin. Epidemiol. 57, 898–910 (2004).

    Article 
    PubMed 

    Google Scholar     

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  • A mechanism for hypoxia-induced inflammatory cell death in cancer

    A mechanism for hypoxia-induced inflammatory cell death in cancer

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  • Bertout, J. A., Patel, S. A. & Simon, M. C. The impact of O2 availability on human cancer. Nat. Rev. Cancer 8, 967–975 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Singleton, D. C., Macann, A. & Wilson, W. R. Therapeutic targeting of the hypoxic tumour microenvironment. Nat. Rev. Clin. Oncol. 18, 751–772 (2021).

    Article 
    PubMed 

    Google Scholar     

  • Banh, R. S. et al. PTP1B controls non-mitochondrial oxygen consumption by regulating RNF213 to promote tumour survival during hypoxia. Nat. Cell Biol. 18, 803–813 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Asselman, C., Hemelsoet, D., Eggermont, D., Dermaut, B. & Impens, F. Moyamoya disease emerging as an immune-related angiopathy. Trends Mol. Med. 28, 939–950 (2022).

    Article 
    PubMed 

    Google Scholar     

  • Schofield, C. J. & Ratcliffe, P. J. Oxygen sensing by HIF hydroxylases. Nat. Rev. Mol. Cell Biol. 5, 343–354 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Wouters, B. G. & Koritzinsky, M. Hypoxia signalling through mTOR and the unfolded protein response in cancer. Nat. Rev. Cancer 8, 851–864 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Cummins, E. P. et al. Prolyl hydroxylase-1 negatively regulates IkappaB kinase-beta, giving insight into hypoxia-induced NFkappaB activity. Proc. Natl Acad. Sci. USA 103, 18154–18159 (2006).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Rius, J. et al. NF-kappaB links innate immunity to the hypoxic response through transcriptional regulation of HIF-1alpha. Nature 453, 807–811 (2008).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Eltzschig, H. K. & Carmeliet, P. Hypoxia and inflammation. N. Engl. J. Med. 364, 656–665 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Kroemer, G., Galluzzi, L., Kepp, O. & Zitvogel, L. Immunogenic cell death in cancer therapy. Annu. Rev. Immunol. 31, 51–72 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Nizet, V. & Johnson, R. S. Interdependence of hypoxic and innate immune responses. Nat. Rev. Immunol. 9, 609–617 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Pakos-Zebrucka, K. et al. The integrated stress response. EMBO Rep. 17, 1374–1395 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Delibegovic, M., Dall’Angelo, S. & Dekeryte, R. Protein tyrosine phosphatase 1B in metabolic diseases and drug development. Nat. Rev. Endocrinol. 20, 366–378 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

    Article 
    ADS 
    PubMed Central 

    Google Scholar     

  • Ahel, J. et al. Moyamoya disease factor RNF213 is a giant E3 ligase with a dynein-like core and a distinct ubiquitin-transfer mechanism. eLife 9, e56185 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Otten, E. G. et al. Ubiquitylation of lipopolysaccharide by RNF213 during bacterial infection. Nature 594, 111–116 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Morito, D. et al. Moyamoya disease-associated protein mysterin/RNF213 is a novel AAA+ ATPase, which dynamically changes its oligomeric state. Sci. Rep. 4, 4442 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Piccolis, M. et al. Probing the global cellular responses to lipotoxicity caused by saturated fatty acids. Mol. Cell 74, 32–44 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Sugihara, M. et al. The AAA+ ATPase/ubiquitin ligase mysterin stabilizes cytoplasmic lipid droplets. J. Cell Biol. 218, 949–960 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Thery, F. et al. Ring finger protein 213 assembles into a sensor for ISGylated proteins with antimicrobial activity. Nat. Commun. 12, 5772 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Takeda, M. et al. Moyamoya disease patient mutations in the RING domain of RNF213 reduce its ubiquitin ligase activity and enhance NFkappaB activation and apoptosis in an AAA+ domain-dependent manner. Biochem. Biophys. Res. Commun. 525, 668–674 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • LaMontagne, K. R. Jr, Hannon, G. & Tonks, N. K. Protein tyrosine phosphatase PTP1B suppresses p210 bcr-abl-induced transformation of rat-1 fibroblasts and promotes differentiation of K562 cells. Proc. Natl Acad. Sci. USA 95, 14094–14099 (1998).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Kotani, Y. et al. Alternative exon skipping biases substrate preference of the deubiquitylase USP15 for mysterin/RNF213, the moyamoya disease susceptibility factor. Sci. Rep. 7, 44293 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Kovalenko, A. et al. The tumour suppressor CYLD negatively regulates NF-kappaB signalling by deubiquitination. Nature 424, 801–805 (2003).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Schlicher, L., Brauns-Schubert, P., Schubert, F. & Maurer, U. SPATA2: more than a missing link. Cell Death Differ. 24, 1142–1147 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Louvrier, C. et al. RNF213-associated urticarial lesions with hypercytokinemia. J. Allergy Clin. Immunol. 150, 1545–1555 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Bhardwaj, A., Banh, R. S., Zhang, W., Sidhu, S. S. & Neel, B. G. MMD-associated RNF213 SNPs encode dominant-negative alleles that globally impair ubiquitylation. Life Sci. Alliance 5, e202000807 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Cecchi, A. C. et al. RNF213 rare variants in an ethnically diverse population with Moyamoya disease. Stroke 45, 3200–3207 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Kamada, F. et al. A genome-wide association study identifies RNF213 as the first Moyamoya disease gene. J. Hum. Genet. 56, 34–40 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Liu, W. et al. Identification of RNF213 as a susceptibility gene for moyamoya disease and its possible role in vascular development. PLoS ONE 6, e22542 (2011).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Taniguchi, K. & Karin, M. NF-kappaB, inflammation, immunity and cancer: coming of age. Nat. Rev. Immunol. 18, 309–324 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • He, Y., Hara, H. & Nunez, G. Mechanism and regulation of NLRP3 inflammasome activation. Trends Biochem. Sci 41, 1012–1021 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Karki, R. & Kanneganti, T. D. Diverging inflammasome signals in tumorigenesis and potential targeting. Nat. Rev. Cancer 19, 197–214 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Diaz-Rodriguez, E. et al. TRAIL receptor activation overcomes resistance to trastuzumab in HER2 positive breast cancer cells. Cancer Lett. 453, 34–44 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Endo, M., Mori, M., Akira, S. & Gotoh, T. C/EBP homologous protein (CHOP) is crucial for the induction of caspase-11 and the pathogenesis of lipopolysaccharide-induced inflammation. J. Immunol. 176, 6245–6253 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • An, J. et al. Inactivation of the CYLD deubiquitinase by HPV E6 mediates hypoxia-induced NF-kappaB activation. Cancer Cell 14, 394–407 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Wiede, F. et al. PTP1B is an intracellular checkpoint that limits T-cell and CAR T-cell antitumor immunity. Cancer Discov. 12, 752–773 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Baumgartner, C. K. et al. The PTPN2/PTPN1 inhibitor ABBV-CLS-484 unleashes potent anti-tumour immunity. Nature 622, 850–862 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Fedele, C. et al. SHP2 inhibition prevents adaptive resistance to MEK inhibitors in multiple cancer models. Cancer Discov. 8, 1237–1249 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Marcotte, R. et al. Functional genomic landscape of human breast cancer drivers, vulnerabilities, and resistance. Cell 164, 293–309 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Zhang, S. et al. Genetically defined, syngeneic organoid platform for developing combination therapies for ovarian cancer. Cancer Discov. 11, 362–383 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Martinez, A. M., Kim, A. & Yang, W. S. Detection of ferroptosis by BODIPY 581/591 C11. Methods Mol. Biol. 2108, 125–130 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat. Biotechnol. 36, 880–887 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Teo, G. et al. SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software. J. Proteomics 100, 37–43 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar     

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  • Mechanism of BRCA1–BARD1 function in DNA end resection and DNA protection

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    Cloning, expression and purification of recombinant proteins

    Human wild-type DNA2, helicase-dead DNA2 K654R and nuclease-dead DNA2 D277A were expressed in Sf9 insect cells and purified by affinity chromatography taking advantage of the N-terminal 6×His tag and the C-terminal FLAG tag35. Yeast nuclease-dead Dna2 E675A was expressed in S. cerevisiae and purified using the N-terminal FLAG tag and the C-terminal 6×His tag51. Full-length wild-type WRN, helicase-dead WRN K577M, exonuclease-dead WRN E84A, WRN fragments, BLM, as well as wild-type CtIP and its variants were purified exploiting the MBP tag at the N terminus and 10×His tag at the C terminus23,28,35,38,52,53. The MBP tag was removed during purification by cleavage with PreScission protease. For the expression of phosphorylated wild-type CtIP (pCtIP) and its variants, Sf9 cells were treated with 50 nM Okadaic acid (APExBIO) 3 h before collection to preserve the phosphorylated state of the proteins, and 1 µM camptothecin (Sigma) 1 h before collection to increase the activation of the protein phosphorylation cascade. For the expression of dephosphorylated WRN (λWRN) and CtIP (λCtIP), proteins were incubated with λ phosphatase at room temperature for 30 min during purification. The MRN and MRE11–RAD50 complexes were obtained using the 6×His tag and 3×FLAG tag at the C termini of MRE11 and RAD50, respectively23. Human wild-type EXO1, as well as nuclease-dead EXO1 D173A, were purified using M2 anti-FLAG affinity resin (Sigma) and HiTrap SP HP cation exchange chromatography column (Cytiva)26,54. EXO1Δ1 (Δ353–846) fragment, along with a matched wild-type control, were purified omitting the HiTrap SP HP cation exchange chromatography step. E. coli ExoIII, ScaI and SspI were purchased from New England Biolabs. Wild-type human RAD51, as well as the indicated human RAD51 variants and yeast Rad51, were expressed in BL21 (DE3)pLysS E. coli cells and purified using amylose affinity chromatography followed by HiTrap Q chromatography (Cytiva)48.

    The BRCA1 sequence was codon optimized for the expression in Sf9 cells (Biomatik) with flanked NheI and XmaI restriction sites. The full-length sequence is listed in Supplementary Table 1 provided in the Supplementary Information. The BRCA1 gene was then cloned into pFB-2×MBP-CtIP-10×His55 to generate pFB-2×MBP-BRCA1co-10×His. The cloning created a fusion construct with the 2×MBP tag at the N terminus and the 10×His tag at the C terminus. All BRCA1 variants were cloned from pFB-2×MBP-BRCA1co-10×His using the primers listed in Supplementary Table 3 provided in the Supplementary Information. Similarly, the BARD1 sequence was codon optimized for the expression in Sf9 cells (Supplementary Table 2 provided in the Supplementary Information, Biomatik) with BamHI and XmaI restriction sites. The BARD1 gene was then cloned into pFB-RAD50co-FLAG23 to generate pFB-BARD1co-FLAG (BARD1 with C-terminal FLAG tag). All BARD1 variants were cloned from pFB-BARD1co-FLAG using the primers listed in Supplementary Table 3 provided in the Supplementary Information. The BRCA1–BARD1 complex, BRCA1 on its own and all variants were expressed in Sf9 cells using the SFX Insect serum-free medium (Hyclone) and the Bac-to-Bac expression system (Invitrogen), according to the manufacturer’s recommendations. Frozen Sf9 pellets from 1 l of culture were resuspended in lysis buffer (50 mM Tris-HCl pH 7.5, 1 mM ethylenediaminetetraacetic (EDTA), 1:400 protease inhibitor cocktail (Sigma, P8340), 30 µg ml−1 leupeptin (Merck Millipore), 1 mM phenylmethylsulfonyl fluoride (PMSF), 1 mM dithiothreitol (DTT), 0.5% NP40) and incubated at 4 °C for 20 min. Glycerol was added to a final concentration of 25%, NaCl was added to a final concentration of 325 mM and the cell suspension was incubated at 4 °C for 20 min. The cell suspension was centrifuged at 55,000g at 4 °C for 30 min. The soluble extract was incubated with amylose resin (New England Biolabs) at 4 °C for 1 h. The resin was washed with amylose wash buffer (50 mM Tris-HCl pH 7.5, 2 mM β-mercaptoethanol, 300 mM NaCl, 10% glycerol, 1 mM PMSF). Proteins were eluted using amylose elution buffer (50 mM Tris-HCl pH 7.5, 2 mM β-mercaptoethanol, 300 mM NaCl, 10% glycerol, 1 mM PMSF, 10 mM maltose (Sigma), 20 mM imidazole (Sigma)). The solution was immediately loaded onto pre-equilibrated Ni-NTA agarose resin (Qiagen) at 4 °C, in flow. The resin was washed with Ni-NTA buffer 1 (50 mM Tris-HCl pH 7.5, 2 mM β-mercaptoethanol, 10% glycerol, 1 mM PMSF, 20 mM imidazole and 1 M NaCl for BRCA1 or 0.3 M NaCl for BRCA1–BARD1), and subsequently with Ni-NTA buffer 2 (50 mM Tris-HCl pH 7.5, 2 mM β-mercaptoethanol, 150 mM NaCl, 10% glycerol, 1 mM PMSF, 20 mM imidazole). Proteins were eluted with Ni-NTA elution buffer (50 mM Tris-HCl pH 7.5, 2 mM β-mercaptoethanol, 150 mM NaCl, 10% glycerol, 1 mM PMSF, 200 mM imidazole). Fractions containing high protein concentration as estimated by the Bradford assay were pooled, aliquoted, snap-frozen in liquid nitrogen and stored at −80 °C. The BRCA1–BARD1 mutants were purified in the same way. We note that attempts to cleave the MBP tag before Ni-NTA purification resulted in protein precipitation. We could obtain up to roughly 0.6 mg of BRCA1–BARD1 from 1 l of media (approximate stock concentration, 800 nM). For the expression of dephosphorylated BRCA1–BARD1 (λBRCA1–BARD1), the complex was incubated with λ phosphatase at room temperature for 30 min during purification, along with a matched control that was similarly incubated but without λ phosphatase.

    Human RPA sequence was cloned from p11d–tRPA construct56 using the primers listed in Supplementary Table 3 provided in the Supplementary Information. Whereas both RPA1 and RPA2 were flanked by the BamHI and NheI restriction sites, RPA3 was flanked by SalI and XbaI. These restriction enzymes were used to generate pFB-RPA1, pFB-RPA2 and pFB-6×His-RPA3 insect expression vectors used for the protein purification. RPA was expressed in Sf9 cells in SFX Insect serum-free medium (Hyclone) using the Bac-to-Bac expression system (Invitrogen), according to the manufacturer’s recommendations. A frozen Sf9 pellet from 2 l of culture was resuspended in lysis buffer (50 mM Tris-HCl pH 7.5, 2 mM β-mercaptoethanol, 1:200 protease inhibitor cocktail, 60 µg ml−1 leupeptin, 1 mM PMSF, 20 mM imidazole, 0.1% NP40) and incubated at 4 °C for 20 min. Glycerol was added to a final concentration of 25%, KCl was added to a final concentration of 325 mM and the cell suspension was incubated at 4 °C for 30 min. The cell suspension was centrifuged at 55,000g at 4 °C for 30 min. The soluble extract was incubated with Ni-NTA affinity resin at 4 °C for 1 h. Ni-NTA resin was washed with wash buffer (50 mM Tris-HCl pH 7.5, 2 mM β-mercaptoethanol, 1 mM PMSF, 10% glycerol, 500 mM KCl, 20 mM imidazole, 0.1% NP40). Protein was eluted using wash buffer containing 300 mM imidazole. The eluate was diluted by adding 2 volumes of buffer A (30 mM HEPES pH 7.5, 1 mM DTT, 1 mM PMSF, 10% glycerol, 500 mM KCl, 0.25 mM EDTA, 0.01% NP40). The diluted fractions were purified on a HiTrap Blue HP column (Cytiva) followed by HiTrap desalting column (Cytiva) as described57. Peak desalted fractions were pooled, diluted with 1 volume of buffer B (30 mM HEPES pH 7.5, 1 mM DTT, 1 mM PMSF, 10% glycerol, 0.25 mM EDTA) and loaded onto two 5 ml HiTrap Heparin columns (Cytiva) connected in tandem. Proteins were eluted using a 30 ml gradient of 50 mM to 1 M KCl in 1 ml fractions. Peak fractions were pooled and diluted to a final concentration of roughly 100 mM KCl with buffer B. The diluted eluate was loaded and further purified on a HiTrap Q column (Cytiva) as previously described57. We could obtain roughly 45 mg of human RPA from 2 l insect cells. The sequences of all primers used for cloning in this study are listed in Supplementary Table 3 provided in the Supplementary Information. Purified recombinant proteins were analysed by using SDS–PAGE denaturing electrophoresis and stained with Coomassie Brilliant Blue (VWR). The final images were acquired with a photo scanner operated with Epson Scan v.3.9.4.0 US software and CanoScan 9000F Mark II scanner operated with ImageCapture v.6.6(525) software.

    The sgCtIP (CTCCCGGATCTATACTCCAC) used for depletion of endogenous CtIP in RPE1 EXO1+/+ and RPE1 EXO1−/− cells was cloned into pLentiCRISPR-v2 using BsmBI. The PAM-sequence of this guide RNA (gRNA) was mutated in the full-length pcDNA3.1 CtIP overexpressing constructs (pcDNA3.1_CtIP-WT-2×FLAG and pcDNA3.1_CtIP-S327A-2×FLAG) using site-directed mutagenesis to render the exogenous CtIP expression insensitive to CRISPR-mediated depletion. Subsequently, the coding sequence was cloned into the Gateway entry vector pENTR_1A using KpnI and NotI before transferring it to the destination vector pCW57.1-Zeo using a Gateway LR reaction.

    Sequence analysis of BRCA1 and BARD1 proteins

    Alignment of the BRCA1 region 931–1171 and of the BARD1 region 123–261 were generated using the MAFFT method58 and represented using Jalview59.

    Preparation of DNA substrates

    The sequences of all oligonucleotides used for DNA substrate preparation are listed in Supplementary Table 4 provided in the Supplementary Information. The oligonucleotide-based Y-structured DNA substrate was prepared with the oligonucleotides X12-3HJ3 and X12-3TOPLbis35. The oligonucleotide-based 70 bp-long dsDNA substrate was prepared with the oligonucleotides PC210 and PC211. X12-3HJ3 and PC210 oligonucleotides were 32P-labelled at the 3′ terminus with (α-32P)dCTP (Hartmann Analytic) and terminal transferase (New England Biolabs) according to the manufacturer’s instructions. The oligonucleotide-based 70 bp-long dsDNA biotinylated at the 5′ terminus was prepared using the oligonucleotides PC206 and PC217. PC206 oligonucleotide was 32P-labelled at the 5′ terminus with (γ-32P)ATP (Hartmann Analytic) and T4 PNK (New England Biolabs) according to the manufacturer’s instructions. The randomly labelled 2.2 kbp-long substrate was prepared by amplifying the human NBS1 gene by PCR reaction containing 66 nM (α-32P)dCTP (Hartmann Analytic) with the standard dNTPs concentration (200 µM each)27. When randomly labelled ssDNA was required, the 2.2 kbp-long substrate was heated at 95 °C for 5 min before the experiments. The HindIII digest of λ DNA (New England Biolabs) was labelled by fill-in at the 3′ end with (α-32P)dCTP (Hartmann Analytic), dGTP, dATP (0.25 mM each) and 5 U of the Klenow fragment of DNA polymerase I exo- (lacking the 3′–5′ and 5′–3′ exonuclease activities of DNA polymerase I) (New England Biolabs). Unincorporated nucleotides were removed with Micro Bio-Spin P-30 Tris chromatography columns (BioRad). When the heat-denatured substrate was needed, the substrate was incubated at 95 °C for 5 min to obtain ssDNA27. pUC19-based dsDNA substrate was prepared by digesting the pUC19 plasmid with HindIII-HF restriction enzyme (New England Biolabs) according to the manufacturer’s instructions, and purified by phenol-chloroform extraction and ethanol precipitation. The resulting linear dsDNA was labelled by fill-in at the 3′ end with 0.25 mM of (α-32P)dCTP (Hartmann Analytic), dGTP, dATP and 5 U of the Klenow fragment of DNA polymerase I exo- (New England Biolabs). Unincorporated nucleotides were removed using Micro Bio-Spin P-30 Tris chromatography columns (BioRad). For the ATPase assay with wild-type DNA2 and helicase-dead DNA2 D277A, the 10.3 kbp-long pFB-MBP-hMLH3 plasmid60 was linearized with NheI (New England Biolabs) and purified with QIAquick PCR purification kit (Qiagen). The substrate was denatured at 95 °C for 5 min to obtain ssDNA. The overhanging substrate used for single-molecule magnetic tweezer experiments was prepared as previously described61,62. Briefly, the main 6.6 kbp-long fragment was prepared from pNLRep plasmid63 using the restriction enzymes BamHI and BsrGI (New England Biolabs). Furthermore, a 63 nt-long ssDNA gap was introduced using the nicking enzyme Nt.BbvCI (New England Biolabs). The gap was then filled by hybridizing a 25 nt-long DNA oligomer carrying an extra 40 nt-long polythimidine tail at the 5′ end (overhang), followed by 3′ end ligation inside the gap. Subsequently, 600 bp-long DNA handles carrying either several digoxigenin or biotin modifications were attached at either end. The handles were produced by PCR using as a template the plasmids pBlueScript II SK+ (digoxigenin, Dig handle Forward and Dig handle Reverse primers) or pNLRep (biotin, Bio handle Forward and Bio handle Reverse primers), respectively, in the presence of digoxigenin and biotin-modified nucleotides and digested with BamHI or BsrGI (New England Biolabs), respectively. The final construct shows the 5′ overhang at roughly 0.5 kbp distance from the surface attachment handle.

    DNA end resection and protection assays

    DNA endonuclease assays with the MRN complex and pCtIP were performed in 15 µl volume in nuclease buffer containing 25 mM Tris-acetate pH 7.5, 5 mM magnesium acetate, 1 mM manganese acetate, 1 mM ATP, 1 mM DTT, 0.25 mg ml−1 bovine serum albumin (BSA) (New England Biolabs), 1 mM phosphoenolpyruvate (PEP), 80 U ml−1 pyruvate kinase (Sigma) and 1 nM substrate (in molecules). Biotinylated DNA ends were blocked by adding 15 nM monovalent streptavidin (a kind gift from M. Howarth, University of Oxford)64 and by incubating the samples at room temperature for 5 min. Different from above, DNA exonuclease assays with recombinant MRE11–RAD50 were carried out in nuclease buffer containing 3 mM manganese acetate. Recombinant proteins were added on ice and the reactions were incubated at 37 °C for 2 h. Reactions were stopped by adding 0.5 µl of 0.5 M EDTA and 1 μl Proteinase K (Roche, 18 mg ml−1), and incubated at 50 °C for 30 min. An equal amount of formamide dye (95% [v/v] formamide, 20 mM EDTA, bromophenol blue) was added, samples were heated at 95 °C for 4 min and separated on 15% denaturing polyacrylamide gels (ratio acrylamide:bisacrylamide 19:1, BioRad). After fixing in a solution containing 40% methanol, 10% acetic acid and 5% glycerol for 30 min, the gels were dried on 3MM paper (Whatman), exposed to storage phosphor screens (GE Healthcare) and scanned with Typhoon FLA 9500 Phosphor Imager (GE Healthcare).

    DNA end-resection assays with PCR-based or pUC19-based dsDNA substrate were performed in a 15 µl volume in 25 mM Tris-acetate pH 7.5, 2 mM magnesium acetate, 1 mM ATP, 1 mM DTT, 0.1 mg ml−1 BSA, 1 mM PEP, 80 U ml−1 pyruvate kinase and 1 nM substrate (in molecules). NaCl was added to the reaction buffer to a final concentration of 50 mM (unless indicated otherwise) taking into account the salt coming from protein storage or dilution buffers. When randomly labelled ssDNA was used, 2 nM substrate (in molecules) was used. Where indicated, AMP-PNP (Toronto Research Chemicals) or ATP-γ-S (Cayman Chemical) were used instead of ATP. Human RPA was included to saturate all ssDNA, as indicated. Further recombinant proteins were then added on ice and the reactions were incubated at 37 °C for 30 min, unless indicated otherwise. Reactions were stopped by adding 5 µl of 2% stop solution (150 mM EDTA, 2% SDS, 30% glycerol, bromophenol blue) and 1 µl of Proteinase K (Roche, 18 mg ml−1) and incubated at 37 °C for 15 min. Samples were analysed by 1% agarose gel electrophoresis. Gels were dried on DE81 chromatography paper (Whatman) and analysed as described above.

    The nuclease assays with λ DNA/HindIII-based substrates were carried out similarly as described above with the following differences. DNA was used at 0.15 nM (in molecules), the reaction buffer contained 3 mM magnesium acetate, 30 mM NaCl and, unless indicated otherwise, reactions were incubated at 37 °C for 1 h. DNA protection assays with PCR-based dsDNA substrate were carried out as indicated above for the respective DNA end resection assays, except RAD51, BRCA1–BARD1 or BRCA1 were pre-incubated at 37 °C for 10 min before the addition of the other recombinant proteins. Protection reactions were stopped by adding 0.5 µl of 0.5 M EDTA and 1 μl of Proteinase K (Roche, 18 mg ml−1), and incubated at 50 °C for 30 min. An equal amount of formamide dye (95% [v/v] formamide, 20 mM EDTA, bromophenol blue) was added, and samples were heated at 95 °C for 4 min and separated on 20% denaturing polyacrylamide gels (ratio acrylamide:bisacrylamide 19:1). After fixing in a solution containing 40% methanol, 10% acetic acid and 5% glycerol for 30 min, the gels were dried on 3MM paper (Whatman) and analysed as described above. Protection assays with pUC19-based dsDNA substrate were carried out as indicated above for the respective DNA end resection assays. Signals were quantified using ImageJ2 (National Institutes of Health, NIH) and plotted with Prism 10 (GraphPad).

    Helicase assays

    Helicase assays with the oligonucleotide-based Y-structured DNA substrate were performed in 15 µl volume in reaction buffer (25 mM Tris-acetate pH 7.5, 5 mM magnesium acetate, 1 mM ATP, 1 mM DTT, 0.1 mg ml−1 BSA, 1 mM PEP, 80 U ml−1 pyruvate kinase and 50 mM NaCl) with 0.1 nM DNA substrate (in molecules). Recombinant proteins were added as indicated. Reactions were incubated at 37 °C for 30 min and stopped by adding 5 µl of 2% stop solution (150 mM EDTA, 2% SDS, 30% glycerol, bromophenol blue) and 1 µl of Proteinase K (Roche, 18 mg ml−1) and incubated at 37 °C for 10 min. To avoid re-annealing of the substrate, the 2% stop solution was supplemented with a 20-fold excess of the unlabelled oligonucleotide with the same sequence as the 32P-labelled one. The products were separated by 10% polyacrylamide gel electrophoresis, dried on 17 CHR chromatography paper (Whatman) and analysed as described for resection assays. Helicase assays with PCR-based, pUC19-based dsDNA substrate or HindIII digest of λ DNA were performed as described for the respective DNA end resection assays. Signals were quantified using ImageJ2 (NIH) and plotted with Prism 10 (GraphPad).

    ATPase assays

    ATPase assays with recombinant WRN were performed in 25 mM Tris-acetate pH 7.5, 5 mM magnesium acetate, 1 mM DTT, 0.1 mg ml−1 BSA, 1 mM ATP, 100 mM NaCl, 1 nM of (γ-32P)ATP (Hartmann Analytic) and 0.1 nM (in molecules) of the X12-3HJ3 oligonucleotide used to prepare the Y-structured DNA substrate used in the helicase assays. RPA and BRCA1–BARD1 or BRCA1 were added on ice and samples were pre-incubated at 37 °C for 10 min. WRN was then added and reactions were incubated at 37 °C for 30 min. ATPase assays with recombinant wild-type DNA2 and nuclease-dead DNA2 D277A were performed in 25 mM Tris-acetate pH 7.5, 3 mM magnesium acetate, 1 mM DTT, 0.1 mg ml−1 BSA, 1 mM ATP, 20 mM NaCl, 1 nM of (γ-32P)ATP (Hartmann Analytic) and 0.32 nM (in molecules) of a heat-denatured 10.3 kbp-long dsDNA as a substrate. RPA and indicated proteins were added on ice and samples were incubated at 37 °C for 15 min. Reactions were stopped with 1.1 µl of 0.5 M EDTA, and separated using thin layer chromatography plates (Merck) with 0.3 M LiCl and 0.3 M formic acid as the mobile phase. Dried plates were exposed to storage phosphor screens (GE Healthcare) and scanned with Typhoon FLA 9500 Phosphor Imager (GE Healthcare). Signals were quantified using ImageJ2 (NIH) and plotted with Prism 10 (GraphPad).

    Protein-interaction assays

    To test the interaction between BRCA1–BARD1 and WRN or EXO1, 1 μg of anti-BRCA1 antibody (Santa Cruz Biotechnology, sc-6954) or anti-WRN antibody (Cell Signaling, 4666S) were captured on 10 μl Protein G magnetic beads (Dynabeads, Invitrogen) by incubating at 4 °C for 1 h with gentle rotation in 50 μl of PBS-T (PBS with 0.1% Tween-20, Sigma). The beads were washed twice on a magnetic rack with 150 μl of PBS-T. The beads were then mixed with 1 μg of the bait in 60 μl of immunoprecipitation buffer (25 mM Tris-HCl pH 7.5, 1 mM DTT, 3 mM EDTA, 0.20 μg μl−1 BSA, 100 mM NaCl) and incubated at 4 °C for 1 h with gentle rotation. Beads were washed three times with 150 μl of wash buffer (25 mM Tris-HCl pH 7.5, 1 mM DTT, 3 mM EDTA, 80 mM NaCl, 0.05% Triton-X, Sigma). Then 1 μg of the prey was added to the beads in 60 μl of immunoprecipitation buffer (25 mM Tris-HCl pH 7.5, 1 mM DTT, 3 mM EDTA, 0.20 μg μl−1 BSA, 100 mM NaCl) and incubated at 4 °C for 1 h with gentle rotation. Beads were again washed three times with 150 μl of wash buffer (25 mM Tris-HCl pH 7.5, 1 mM DTT, 3 mM EDTA, 80 mM NaCl, 0.05% Triton-X) and proteins were eluted by boiling the beads in SDS buffer (50 mM Tris-HCl pH 6.8, 1.6% SDS, 100 mM DTT, 10% glycerol, 0.01% bromophenol blue) at 95 °C for 3 min. Avidin (Sigma) was added to the eluate as a stabilizer. The eluate was separated on a 7.5% SDS–PAGE gel and proteins were detected by western blotting using anti-BRCA1 antibody (Santa Cruz Biotechnology, sc-6954, 1:1,000), anti-His antibody (Invitrogen PA1-983B, 1:1,000) or anti-FLAG antibody (Sigma, F3165, 1:1,000). The final images were acquired with Fusion FX7 capture software (Vilber Imaging).

    Mass photometry characterization of protein complexes

    Mass photometry measurements were performed on a TwoMP mass photometer (Refeyn Ltd). First, borosilicate microscope glass plate (No. 1.5 H thickness, 24 × 50 mm, VWR) were cleaned by sequential soaking in Milli-Q-water, isopropanol and Milli-Q-water followed by drying under a stream of clean nitrogen. Next, silicone gaskets (CultureWell Reusable Gasket, Grace Bio-Labs) were placed on the clean coverslip to create a defined well for sample delivery. To convert optical reflection-interference contrast into a molecular mass, a known protein size marker (NativeMark Unstained Protein Standard, Invitrogen) was measured on the same day. For mass measurements, gaskets were filled with 18 μl of measurement buffer (25 mM Tris-HCl pH 7.5, 1 mM ATP, 3 mM magnesium acetate) to allow focusing the microscope onto the coverslip surface. Subsequently, 40 nM of either BRCA1 or BRCA1–BARD1 were added into the well (final volume, 20 μl) and sample binding to the coverslip surface was monitored for 1 min using the software AcquireMP (Refeyn Ltd). Data analysis was performed using DiscoverMP software (Refeyn Ltd).

    Single-molecule magnetic tweezer experiments

    Single-molecule magnetic tweezer experiments were carried out in a custom-built magnetic tweezers setup and operated using a self-developed code in Labview (2016, National Instruments)65. The DNA constructs were linked at their biotinylated ends with streptavidin-coated magnetic beads (Dynabeads M280, Thermo Fisher Scientific) and flushed into the flow cell, where the bottom slide was coated with antidigoxigenin to ensure surface-specific binding. Moving the magnet closer to the flow cell resulted in the stretching of the DNA molecules that were attached to a magnetic bead. Tracking of the magnetic beads for all measurements was conducted at 300 Hz using video microscopy and real-time GPU-accelerated image analysis66. The magnetic forces were calibrated based on fluctuation analysis67. The measurements were performed in a reaction buffer (25 mM Tris-acetate pH 7.5, 2 mM magnesium acetate, 1 mM ATP, 1 mM DTT, 0.1 mg ml−1 BSA), with the indicated protein concentrations at a temperature of 37 °C and forces between 15 and 25 pN. The analysis of the recorded traces was conducted with a custom written MATLAB program68. We considered only traces from measurements in which the magnetic bead position was traceable for at least 300 s. The acquired processivity and velocity for the unwinding events were calculated by fitting linear segments to parts of the recorded traces with roughly constant velocity, which were used to construct the histograms and for statistical analysis. To quantify the ratio of rewinding/unwinding events, the total number of the two events, acquired as described above, was determined for a fixed period of 300 s for each recorded trace. To characterize the different protein combinations (Fig. 3c) and WRN variants (Extended Data Fig. 7e), the difference between the maximum value and the minimum value of DNA extension for a given molecule was calculated during the first 300 s and expressed as ΔDNA-length. Each dot represents one measured molecule.

    Cell lines

    The RPE1 hTERT were purchased from American Type Culture Collection (ATCC). The RPE1 hTERT PAC−/−TP53−/− cell line (referred to as RPE1 EXO1+/+ in this paper)45 was used to generate RPE1 hTERT PAC−/−TP53−/−EXO1−/− (referred to as RPE1 EXO1−/−) cells by nucleofection of pLentiCRISPR_v2 containing the sgEXO1 (GCGTGGGATTGGATTAGCAA) as described before45. After clonal selection, genotyping was performed to confirm indel formation using target locus PCR amplification and Sanger sequencing, followed by TIDE (tracking of indels by decomposition) analysis. RPE1 EXO1+/+ and EXO1−/− cells inducibly expressing exogenous CtIP-WT or CtIP-S327A were obtained by viral transduction with pCW57.1_Zeo-CtIP-WT-2×FLAG or pCW57.1_Zeo-CtIP-S327A-2×FLAG.

    U2OS cells were originally bought from ATCC. U2OS-derived cells, carrying green fluorescent protein (GFP), GFP-CtIP-WT or GFP-CtIP-S327A mutant16, were grown in DMEM medium (Sigma). Media were supplemented with 10% fetal bovine serum (Sigma), 2 mM l-glutamine (Sigma), 100 U ml−1 penicillin and 100 μg ml−1 streptomycin (Sigma). U2OS cells were last authenticated in June 2024 by the GenePrint 10 System (Promega) using short tandem repeat profiling, and data were analysed using genemapper id-x v.1.2 software (Applied Biosystems) at the genomic core facility of the Instituto de Investigaciones Biomedicas Sols-Morreale. All cell lines were routinely tested for mycoplasm contamination. All the experiments performed here used mycoplasm-free cell lines.

    Viral transductions and transfections

    Third-generation packaging vectors pMDLg/pRRE, pRSV-Rev, pMD2.g and a lentiviral expression vector (pLentiCRISPR-v2 or pCW57.1) were transfected to human embryonic kidney (HEK) 293T using jetPEI (Polyplus Transfection) to produce lentiviral particles. The HEK 293T cell line was originally purchased from ATCC. The medium was refreshed 16 h post-transfection. Viral supernatants were harvested 48 h post-transfection, filtered with a 0.45 mm filter and transduced into cells at a multiplicity of infection of 1 in the presence of 4 μg ml−1 polybrene. Puromycin (2 μg ml−1) and zeocin (400 μg ml−1) were used for the selection of pLentiCRISPR- and pCW57.1- transduced RPE1 cells, respectively.

    Clonogenic survival assays

    RPE1 EXO1+/+ or EXO1−/− cells transduced with CtIP-WT or CtIP-S327A were induced with doxycycline (2 μg ml−1) to express CtIP protein exogenously. Cells were virally transduced with pLentiCRISPR-sgCtIP or empty vector to deplete endogenous CtIP 24 h post-doxycycline induction. After 48 h of puromycin selection to select for pLentiCRISPR transduced cells, 500 cells were seeded in 10-cm dishes for clonogenic growth. Medium containing doxycycline (2 μg ml−1) was refreshed after 7 days. After 14 days, colonies were stained with crystal violet solution (0.4% [w/v], 20% methanol) and counted manually. Simultaneously with plating cells for clonogenic survival, cells were collected for immunoblotting analysis and lysed in RIPA lysis buffer (1% NP40, 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.1% SDS, 3 mM MgCl2, 0.5% sodium deoxycholate) supplemented with Complete Protease Inhibitor Cocktail (Sigma) and 100 U ml−1 Benzonase (Sigma). Western blots were stained with primary antibodies against CtIP (Millipore, MABE1060, 1:2,000), FLAG (Sigma, F1804-200UG, 1:2,000), EXO1 (Abcam, ab95068, 1:1,000) and α-Tubulin (Sigma, T6199, 1:5,000); and with HRP-conjugated secondary antibodies donkey anti-rabbit IgG-HRP (Thermo Scientific, 31458, 1:5,000) or goat anti-mouse IgG-HRP (Thermo Scientific, 31432, 1:5,000).

    Immunofluorescence and microscopy

    For RPA foci visualization, U2OS-derived cells were seeded on coverslips. For the experiment with DNA2 inhibitor C5 (MedChemExpress, catalogue no. HY128729), 20 μM of the inhibitor or the same amount of vehicle (dimethylsulfoxide, DMSO) were added to the plates 6 h before irradiation. Then 1 h after irradiation (10 Gy), coverslips were washed once with PBS followed by treatment with pre-extraction buffer (25 mM Tris-HCl pH 7.5, 50 mM NaCl, 1 mM EDTA, 3 mM MgCl2, 300 mM sucrose and 0.2% Triton-X-100) for 5 min on ice. Cells were fixed with 4% paraformaldehyde [w/v] in PBS for 20 min. Following two washes with PBS, cells were blocked for 1 h with 5% fetal bovine serum in PBS, costained with the appropriate primary antibodies (RPA2, Abcam, ab2175, 1:500) in blocking solution overnight at 4 °C or for 2 h at room temperature, washed again with PBS and then co-immuno-stained with the appropriate secondary antibodies (Alexa Fluor 594 goat anti-mouse, Invitrogen, A11032, 1:500 and Alexa Fluor 488 goat anti-rabbit, Invitrogen, A11034, 1:500) in blocking buffer. After washing with PBS, coverslips were incubated sequentially in 70% and 100% ethanol to dehydrate them. Finally, they were air dried and mounted into glass slides using Vectashield mounting medium with 4,6-diamidino-2-phenylindole (Vector Laboratories). RPA foci immunofluorescence was analysed using a Leica DM6000B Fluorescence microscope (AF6000).

    Cell-cycle analysis

    Cells were trypsinized and fixed with cold 70% ethanol overnight, incubated with 250 μg ml−1 RNase A (Sigma) and 10 μg ml−1 propidium iodide (Fluka) at 37 °C for 30 min and analysed with a LSRFortessaTM Cell Analyzer (BD) Flow Cytometer. Cell-cycle distribution data were further analysed using ModFit LT v.5.0 software (Verity Software House Inc.).

    Statistics and reproducibility

    Sample size or number of technical (for biochemical assays) and biological (for cellular assays) replicates were chosen on the basis of what is common in the field and what was practical to do. A minimum of three independent replicates were performed for each biochemical experiment to add statistical analysis, when required. Where indicated, a representative experiment from independent repeats with similar results was shown. Coomassie-stained protein gels were repeated twice to confirm the quality and the concentration of the indicated recombinant proteins. Protein-interaction assays were performed twice.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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  • Mechanisms that clear mutations drive field cancerization in mammary tissue

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    Mice

    All mice used for experiments were adult females from a mixed background, housed under standard laboratory conditions and receiving food and water ad libitum. All experiments were performed in accordance with the guidelines of the Animal Welfare Committees of the Netherlands Cancer Institute and KU Leuven. Sample size was determined using a resource equation approach, mice were randomly assigned to experimental groups and blinding was performed during data analysis. R26R-Confettihet (JAX stock no. 013731)61,62; R26-CreERT2het (JAX stock no. 008463)63 mice were injected intraperitoneally with tamoxifen (Sigma-Aldrich), diluted in sunflower oil, to activate Cre recombinase. To achieve clonal density labelling (fewer than one MaSC per duct on average), R26R-Confettihet;R26-CreERT2het mice were injected with 1 mg of tamoxifen per 25 g of body weight between 10 and 15 weeks of age. Ovariectomies were performed between 10 and 15 weeks of age, at least 7 days before lineage tracing initiation. The third, fourth and fifth mammary glands of R26R-Confetti;Brca1fl/fl;Trp53fl/fl (refs. 31,64) or R26R-Confetti mice were intraductally injected with recombinant TAT-Cre protein (20 units per gland diluted in 20 µl of PBS, Sigma-Aldrich) between 10 and 15 weeks of age. This TAT-Cre injection resulted in roughly one labelled cell for every 100–200 cells (Extended Data Fig. 4e). As the confetti construct comprises four distinct colours, there is, on average, one cell labelled with a confetti colour per 400 cells. Considering that a MaSC-progeny unit consists of roughly five to ten cells, a single confetti-labelled cell is induced in one out of 40–80 units. Over time, many clones become extinct, leading to a dilution in the number of clones and making collisions even less likely. For each of the experiments, mice were analysed at different time points after lineage tracing initiation as indicated in Fig. 1b. The injected mammary glands of R26R-Confetti;Brca1fl/fl;Trp53fl/fl mice at the latest time point (225 days) were analysed when one of the injected glands developed a palpable tumour of at least 5 × 5 mm, which was between 200 and 250 days after recombination. Tumour sizes did not exceed 1,500 mm3 in accordance with the guidelines of the Animal Welfare Committee of the Royal Netherlands Academy of Arts and Sciences, the Netherlands Cancer Institute and KU Leuven. Samples were randomly allocated to the experimental groups, sample size was not determined a priori and investigators were not blinded to experimental conditions, except where indicated. For clonal analysis of the R26R-Confetti;Brca1fl/fl;Trp53fl/fl model, we analysed n = 4 mice (14 days), n = 4 mice (64 days), n = 5 mice (120 days) and n = 6 mice (225 days). For clonal analysis of the R26R-Confetti model, we analysed n = 3 mice (14 days), n = 3 mice (64 days), n = 5 mice (120 days) and n = 6 mice (225 days). Clonal analysis of the ovariectomized R26R-Confetti;Brca1fl/fl;Trp53fl/fl and R26R-Confettihet models was performed on at least n = 3 mice per time point. Adult CAG;;KikGR female mice65 (RIKEN no. CLSTCDB0201T-117830853340) were used to visualize the short-term dynamics of the mammary gland by repeated imaging through a mammary imaging window as described below. R26-mTmG female mice (JAX no. 007676)66 were used to visualize the long-term stability of the mammary gland through a repeated skin-flap procedure as described below.

    Mammary imaging window implantation, repeated skin-flap procedure and intravital imaging

    Mice were anaesthetized using isoflurane (Isovet) inhalation (1.5/2% isoflurane/air mixture). The fourth mammary gland of adult CAG;;KikGR female mice was imaged repeatedly through a mammary imaging window as previously described47,48. The fourth mammary gland of adult R26-mTmG mice was imaged repeatedly with a skin flap as previously described47. To visualize the mammary gland, mice were placed in a facemask within a custom designed imaging box. Isoflurane was introduced through the facemask and ventilated by an outlet on the other side of the box. The imaging box and microscope were kept at 34 °C by a climate chamber surrounding the entire stage of the microscope including the objectives. Imaging was performed on an inverted Leica SP8 Dive system (Leica Microsystems) equipped with four tuneable hybrid detectors, a MaiTai eHP DeepSee laser (Spectra-Physics) and an InSight X3 laser (Spectra-Physics) using the Leica Application Suite X (LAS X) software. All images were collected at 8 bit and acquired with a ×25 water immersion objective with a free working distance of 2.40 mm (HC FLUOTAR L ×25/0.95W VISIR 0.17). For the CAG;;KikGR model, Kikume Green was excited at 960 nm and detected at 490–550 nm. Each imaging session, all visible ducts through the imaging window were imaged using a tiled z scan with ×1–2 zoom and a z-step size of 5–10 μm. For the skin-flap imaging, TdTomato was excited at 1,040 nm and detected at 540–730 nm. All visible ducts were imaged together in one tile z scan with a ×0.75 zoom, a z-step size of 10–20 μm. These parameters allowed to scan large regions of up to 2 cm2 in less than 3 h. At the end of the first skin-flap imaging session, the skin was closed with a continuous, non-resorbable suture. After 3 months, the skin flap was re-opened for the second imaging session, and the same imaging fields were retraced using the nipple and collagen I structures of the first imaging session as landmarks.

    Staging of the mice

    To determine the oestrous cycle stage of the mice, a vaginal swab was collected as described67. In short, the vagina was flushed using a plastic pipette filled with 50 µl PBS, and the liquid was transferred to a dry glass slide. After air drying, the slide was stained with Crystal Violet and the cell cytology was examined using a light microscope.

    Clone isolation and CNA sequencing

    Here, 225 days after Cre mediated recombination, fourth mammary glands were extracted, fixed overnight in 1% paraformaldehyde (PFA), incubated in sucrose overnight and stored in optimal cutting temperature (OCT) at −80 °C. For microdissection of individual clones, OCT blocks were thawed at room temperature in the dark for 30 min and mammary glands removed from OCT and washed in 50 ml of PBS on ice. Mammary glands were dissected under a benchtop fluorescent macroscope (Zeiss) using Dumont forceps and fine scissors, using the clone morphology to distinguish between transformed and untransformed clones. Each dissected clone was washed in 1 ml of PBS on ice for 2–5 h (until the end of the dissection procedure). Per mammary gland, one piece of non-fluorescent tissue from the inguinal lymph node was dissected as internal sequencing control. After washing, pieces were lysed in 70 µl of Arcturus lysis buffer following the instructions of the ThermoFisher Scientific Arcturus PicoPure kit KIT0103. Lysis was carried out in a PCR cycler for 18 h at 65 °C followed by 30 min at 75 °C and holding at 4 °C. Samples were then purified using the Roche FFPE DNA extraction kit 06650767001 50-588-384 following the manufacturer’s instructions with elution in 25 µl of PCR grade water. For DNA sequencing, library preparation was carried out with a KAPA Hyper kit (Roche; KK8504) according to the manufacturer protocol with four PCR cycles, before the samples were sequenced by low-coverage whole genome sequencing. The copy number alteration (CNA) analysis was conducted in R, using QDNAseq with 50 kb bins and the mm10 mouse reference genome. This methodology yielded copy number values from both normal and transformed clones, along with an internal control sample. Normalization was achieved by first converting copy number values to log2, then subtracting the internal control sample’s values from those of the normal and transformed tumours. We averaged these adjusted copy number values for each replicate across both clone types (13 early normal clones and 13 early transformed clones). Data visualization was executed using the ggplot2 package in R, with specific emphasis on certain chromosomes. Regarding the late-stage tumours published in ref. 40, copy number profiling data corresponding to ten Wap-Cre;Brca1fl/fl;Trp53fl/fl (WB1P) female mice harbouring a WB1P late-stage mammary tumour, along with internal control samples (spleen) was used. The CNA sequence analysis included the use of cutadapt for adaptor sequence removal and BWA for sequence alignment (using bwa aln, bwa mem) to the mm10 mouse genome. This procedure mirrored the earlier steps up to plotting with ggplot2, repeated for ten WB1P replicates.

    Quantification of the distribution of proliferation

    Oestrous-cycling mice and mice that had undergone ovariectomy with a 2-week recovery period (all above 8 weeks of age) received 0.5 mg ml−1 EdU in drinking water (refreshed every second day) for 1 week. 3D imaging was performed on three cycling mice and five ovariectomized mice. Per mouse, one-quarter of the mammary gland was taken for subsequent analysis. Samples were fixed in 4% PFA overnight and stained using the FLASH protocol with FLASH Reagent 2 (ref. 68). Before adding the primary antibodies, samples were stained for EdU as follows. Tissues were incubated in 5 ml of 3% bovine serum albumin for 1 h, followed by three washes in PBS for 20 min each. EdU was detected with an Alexa-647 azide. The reaction cocktail for EdU fluorescent labelling was prepared according to the manufacturer’s guidelines using the Click-It EdU imaging kit (ThermoFisher Scientific). Per gland, 0.5 ml of reaction cocktail was added for incubation for 4 h at room temperature with gentle agitation on a nutator. The cocktail was removed, samples washed once in 3% bovine serum albumin in PBS for 20 min, followed by three washes in FLASH blocking buffer for 20 min each. Subsequently, samples were stained with primary and secondary antibodies overnight each. Primary antibodies used were KRT8 (rat, Troma-I, Merck Millipore, 1:800) and αSMA (mouse IgG2a, clone 1A4, ThermoFisher Scientific, 1:600). Secondary antibodies used were donkey antirat Alexa-488 and donkey antimouse Alexa546 (ThermoFisher Scientific, catalogue nos. A21208 and A10036, respectively, 1:400), combined with Hoechst 33342 for nucleus detection. Samples were imaged on an Andor Dragonfly spinning disc system, installed on an inverted Leica DMI8 microscope with an Andor Zyla 4+sCMOS camera using a ×10, 0.45 NA Fluo objective (Leica). Imaging was carried out with a 40 μm disc using 405 nm excitation, a 561 nm optically pumped semiconductor laser and 637 nm diode lasers. Images were visualized with Imaris Viewer using gamma correction, ortho slicers and cutting planes to depict deeper tissue layers. For each mammary gland, distribution of proliferation was quantified in five regions. Ripley analysis using QuPath69 was performed with a custom-made script (available at https://github.com/BioImaging-NKI/qupath_ripley). The image was opened in QuPath and a freehand line was drawn by hand to outline the duct for analysis. A multipoint annotation was drawn by hand to mark the positions of proliferating cells along the duct. The script calculated Ripleys K function and normalized it to an unclustered distribution resulting in Ripley’s L function. Data were plotted in GraphPad Prism v.10. For simulations, we have generated clustered and unclustered data in Python.

    Whole-mount immunofluorescence staining of mammary glands

    The third, fourth and fifth mammary glands were dissected and incubated in a mixture of collagenase I (1 mg ml−1, Roche Diagnostics) and hyaluronidase (50 μg ml−1, Sigma-Aldrich) at 37 °C for optical clearance, fixed in periodate–lysine–PFA buffer (1% PFA; Electron Microscopy Science), 0.01 M sodium periodate, 0.075 M l-lysine and 0.0375 M P-buffer (0.081 M Na2HPO4 and 0.019 M NaH2PO4; pH 7.4) for 2 h at room temperature, and incubated for at least 3 h in blocking buffer containing 1% bovine serum albumin (Roche Diagnostics), 5% normal goat serum (Monosan) and 0.8% Triton X-100 (Sigma-Aldrich) in PBS. Primary antibodies were diluted in blocking buffer and incubated overnight at room temperature. Secondary antibodies diluted in blocking buffer were incubated for at least 6 h. Nuclei were stained with 4,6-diamidino-2-phenylindole (DAPI) (0.1 μg ml−1; Sigma-Aldrich) in PBS. Glands were washed with PBS and mounted on a microscopy slide with Vectashield hard set (H-1400, Vector Laboratories). Primary antibodies used were: anti-KRT14 (rabbit, Covance, PRB155P, 1:700), anti-ECAD (rat, eBioscience, 14-3249-82, 1:700), anti-oestrogen receptor (rabbit, no. 13258, Cell Signaling, 1:100), anti-progesterone receptor (rabbit, Clone SP2, MA5-14505, ThermoFisher Scientific, 1:200) and anti-SMA (mouse IgG2a, clone 1A4, Sigma-Aldrich, 1:600). Alexa Fluor 647 and Alexa Fluor 488 Phalloidin were used 1:500 (A-22287 and A-12379, ThermoFisher Scientific) and incubated together with the secondary antibodies. Secondary antibodies used were: goat antirabbit, goat antirat or goat antimouse IgG2a, all conjugated to Alexa-647 (ThermoFisher Scientific, catalogue nos. A21245, A21247 and A21241, respectively, 1:400).

    Whole-mount imaging of mammary glands

    Imaging of whole-mount mammary glands was performed using an inverted Leica TCS SP8 confocal microscope, equipped with a 405 nm laser, an argon laser, a diode-pumped solid-state laser 561 nm laser and a HeNe 633 nm laser. Different fluorophores were excited as follows: DAPI at 405 nm, cyan fluorescent protein (CFP) at 458 nm, green fluorescent protein (GFP) at 488 nm, yellow fluorescent protein (YFP) at 514 nm, red fluorescent protein (RFP) at 561 nm and Alexa-647 at 633 nm. DAPI was collected at 440–470 nm, CFP at 470–485 nm, GFP at 495–510 nm, YFP at 540–570 nm, RFP at 610–640 nm and Alexa-647 at 650–700 nm. All images were acquired with a ×20 (HCX IRAPO N.A. 0.70 WD 0.5 mm) dry objective using a Z-step size of 1–5 μm (total Z-stack around 200 μm). 3D overview tile scans of the mammary glands were acquired by scanning large tile-scan areas (xyz). Next, detailed images were obtained of the individual clones. All images were stitched and processed in the true 3D real-time Rendering LAS X 3D Visualization module (Leica Microsystems) and further processed using ImageJ software (https://imagej.nih.gov/ij/).

    Clonal analysis on whole-mount glands

    Three-dimensional tile-scan images of whole-mount and fully intact mammary glands were used to manually reconstruct the ductal network by outlining the ducts based on the labelling by ECAD, SMA or KRT14 (between 400 and 600 tiles, ×10 objective, Z-step size of 5–10 µm). After localization of the confetti clones in these 3D overview scans, each clone was imaged in detail with a ×25 water objective using confocal imaging by taking a Z-stack with step size between 1 and 3 µm. On the basis of the overlap with the luminal- or basal-cell-specific labelling and cellular morphology (that is, a cuboidal shape for luminal cells and an elongated shape for basal cell), the labelled confetti cells were identified and annotated in the schematic outline of the mammary tree, including information on their confetti colour (GFP, green; YFP, yellow; RFP, red and CFP, cyan) and their identity: that is, luminal or basal. Regions in which, for technical reasons, the gland could not be visualized well were omitted from analysis (in three out of 160 glands). Clone sizes, referring to the number of cells within each clone, were determined through manual visual inspection of tissue samples, with the quantification performed by eye using detailed Z-stack images and 3D rendering of each individual clone. Using custom-made.NET software (available on request from J.v.R.), the coordinates of the branch points, and the position of the labelled cells in ducts and in ductal ends were scored. To calculate the surviving clone fraction, the total number of clones was determined for each of the indicated lineage tracing time points by analysing the entire mammary gland in three dimensions using our whole-gland imaging approach (n = 3 glands per time point of two individual mice). Next, the average numbers of clones identified at 64, 120 and 225 days after lineage tracing initiation were divided by the average number of clones identified 14 days after lineage tracing initiation resulting in the surviving clone fraction as depicted in Figs. 2d and 5g.

    Mammary epithelial cell sorting and real-time qPCR

    The third, fourth and fifth mammary glands of R26R-Confetti;Brca1fl/fl;Trp53fl/fl mice were intraductally injected with recombinant TAT-Cre protein (20 units per gland diluted in 20 µl PBS, produced in-house) between 10 and 13 weeks of age. Then 120 to 180 days after injection, mammary glands were harvested, minced and digested at 37 °C for 30 min in a mixture of collagenase A (2 mg ml−1, Roche Diagnostics), hyaluronidase (300 μg ml−1, Sigma-Aldrich) and DNase (1 mg ml−1) in DMEM/F12 (Gibco). After 10 min incubation with TripLE (Gibco) at 37 °C cells were strained through a 100 μm cell strainer (Fisher Scientific) to obtain single cells. Cells were spun down for 10 min at 550 RCF (relative centrifugal force) at 4 °C followed by blocking for 15 min on ice in 5 mM EDTA/PBS with 2% sterile filtered normal goat serum (Gibco). CD45-Alexa-647 (clone 30-F11, 03123, Biolegend, 1:200) and EpCAM-APC/Cy7 (clone G8.8, 118218, Biolegend, 1:200) were diluted in 5 mM EDTA/PBS with 2% normal goat serum and incubated for 30–45 min on ice to label the immune population (CD45) and the epithelial population (epithelial cell adhesion molecule). Cells were centrifuged for 5 min at 800 RCF at 4 °C and pushed through a 35 μm cell strainer. The FACS Aria III Special Ordered Research Product (BD Biosciences) was used to sort confetti+ and confetti cells, by applying a broad FSC/SSC gate, followed by gates excluding doublets (for the gating strategy, see Extended Data Fig. 1d). Afterwards, non-immune (AF647–; 670/30) confetti-positive (RFP+ (YG610/20), GFP+/YFP+ (BL530/30), CFP+ (V450/50)) and, separately, confetti-negative ((RFP– (YG610/20), GFP–/YFP– (BL530/30), CFP– (V450/50)) epithelial cells (APC/Cy7+; 780/60) were collected. Similarly, non-immune (AF647–; 670/30) epithelial cells (APC/Cy7+; 780/60) were collected from three R26R-Confetti;Brca1fl/fl;Trp53fl/fl mice that had not received TAT-Cre intraductally as a negative control. Data were analysed in FlowJo v.10 for the gating strategy (Extended Data Fig. 1d). Cells were spun down for 10 min at 800 RCF at 4 °C and DNA was isolated using the PicoPure DNA extraction kit (Applied Biosciences; KIT0103) according to the manufacturer’s instructions. The same method was applied to isolate genomic DNA (gDNA) from K14-Cre;Brca1fl/fl;Trp53fl/fl mammary tumour organoids, representing fully recombined samples as a positive control. gDNA concentration was measured using the DeNovix DS-11 spectrophotometer. DNA was diluted to 50–75 ng ml−1 and used for real-time qPCR using the SYBR Green Master Mix (ThermoFisher Scientific, catalogue no. 4309155) in a QuantStudio 6 Flex Real-Time PCR system using the primers listed in the table below. Reactions contained roughly 75 ng of template gDNA and 1 µM of both forward and reverse primers in 20 µl reaction volume. Expression values were calculated by transforming delta–delta Ct values (2-ΔΔCt). Ribosomal Protein L38 (Rpl38) was used as a housekeeping gene. To confirm correct qPCR product amplification, 25 μl of qPCR product of the control samples (R26R-Confetti;Brca1fl/fl;Trp53fl/fl mice that had not received TAT-Cre intraductally) was loaded on an 2% agarose gel with loading buffer (Bioxline, catalogue no. BIO-37045) and a DNA ladder (Meridian Bioscience, catalogue no. BIO-33056) and run at 80 V for 1.5 h, after which the qPCR product was cut out of the gel and purified using the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel, catalogue no. 740609.50) according to the manufacturer’s instructions, and was confirmed by sequencing using the qPCR primers.

    Primer

    Sequence (5′ → 3′)

    BRCA1-ex10-FW

    TGTAACGACAGGCAGGTTCC

    BRCA1-ex10-RV

    ACAGAGTTTGCGGGTGAGTC

    P53-ex5-FW

    AAGACGTGCCCTGTGCAGTT

    P53-ex5-RV

    TCCGTCATGTGCTGTGACTTC

    RPL38_FW

    AGGATGCCAAGTCTGTCAAGA

    RPL38_RV

    TCCTTGTCTGTGATAACCAGGG

    Generation of Brca1;Trp53 mutant and WT organoids followed by Ki-67/CC3 staining

    The fourth and fifth mammary glands of R26R-Confetti;Brca1fl/fl;Trp53fl/fl mice were intraductally injected with recombinant TAT-Cre protein (20 units per gland diluted in 20 µl PBS, Sigma-Aldrich) between 10 and 15 weeks of age. Then, 64 or 225 days post-induction, mammary glands were harvested and prepared for fluorescence-activated cell sorting (FACS) as described before. Both confetti-positive Brca1−/−;Trp53−/− and non-recombined control cells were seeded in a 24-well plate, 10,000 cells per drop of Cultrex Basement Membrane Extract (Type 2, 3532-010-02, R&D Systems) and cultured in the DMEM/F12 (Gibco) supplemented with iInsulin-transferrin-selenium (100×, catalogue no. 41400045, Gibco), B-27 Supplement (50×, catalogue no. 17504044, Gibco), NAC 1.25 mM (N-acetyl-l-cysteine, 0.125 M in PBS, catalogue no. 6169116, Biogems), mFGF2 2.5 nM (Fibroblast Growth Factor 2, catalogue no. 100-18B, PeproTech) and mEGF 2 nM (Epidermal Growth Factor, catalogue no. 3165-09, PeproTech). After 2 weeks of culture, organoids were fixed with 4% PFA (catalogue no. 47347, AlfaAesar) for 10–15 min at room temperature inside the Basement Membrane Extract droplet on an orbital shaker at 25 rpm. Afterwards, organoids were washed three times for 10 min with PBS, followed by incubation in permeabilization buffer (5% normal goat serum (catalogue no. 16210072, Gibco) and 0.5% Triton X-100 ((Sigma-Aldrich) in PBS) for 3 h. To stain for cell proliferation and cell death, primary antibodies Ki-67 (rat, clone SolA15, 14-5698-82, eBioscience, 1:100) and Cleaved Caspase-3 (rabbit, Asp175, no. 9661, Cell Signaling Technology, 1:400), respectively, were added in the blocking buffer (5% normal goat serum (Gibco) in PBS), and incubated overnight at 4 °C. Organoids were washed three times for 15 min with PBS and secondary antibodies goat antirabbit IgG Antibody, Alexa Fluor 647 (catalogue no. A21244, Thermo Scientific, 1:400), goat antirat IgG Antibody, Alexa Fluor 647 (catalogue no. A21247, Thermo Scientific, 1:400) were added and incubated for more than 5 h at room temperature covered in aluminium foil on an orbital shaker at 25 rpm. Organoids were washed three times for 15 min with PBS and stained organoids were mounted by adding 200 µl of Vectashield mounting medium (VECTASHIELD HardSet Antifade Mounting Medium, H-1400, Vector Laboratories). Organoid imaging was performed on an inverted Leica SP8 Dive system (Leica Microsystems), in which Alexa-647 secondary antibodies were excited at 635 nm and detected between 660 and 700 nm, and organoids were imaged using brightfield. The Ki-67/CC3 ratio was derived by first calculating the organoid area and Ki-67+ or CC3+ areas using ImageJ software (https://imagej.nih.gov/ij/), then calculating the percentage of Ki-67 or CC3 expressing cells per organoid, followed by calculation of the Ki-67/CC3 ratio for every organoid.

    Statistics

    P values and statistical tests performed are included in the figure legends or Supplementary Information 1. The longitudinal data of the clone fractions (Figs. 2d and 5g) was analysed using a regression model with a time effect, for which the interaction between time and group was tested. For full details, see Supplementary Information 2. Details on statistics concerning the mathematical modelling can be found in the Supplementary Information 4.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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  • Ayala, G. E. et al. Cancer-related axonogenesis and neurogenesis in prostate cancer. Clin. Cancer Res. 14, 7593–7603 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Huang, D. et al. Nerve fibers in breast cancer tissues indicate aggressive tumor progression. Medicine 93, e172 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Oertel, H. Innervation and tumour growth: a preliminary report. Can. Med. Assoc. J. 18, 135–139 (1928).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Renz, B. W. et al. β2 adrenergic-neurotrophin feedforward loop promotes pancreatic cancer. Cancer Cell 34, 863–867 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Latil, A. et al. Quantification of expression of netrins, slits and their receptors in human prostate tumors. Int. J. Cancer 103, 306–315 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Osswald, M. et al. Brain tumour cells interconnect to a functional and resistant network. Nature 528, 93–98 (2015).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Venkatesh, H. S. et al. Neuronal activity promotes glioma growth through neuroligin-3 secretion. Cell 161, 803–816 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Venkatesh, H. S. et al. Electrical and synaptic integration of glioma into neural circuits. Nature 573, 539–545 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Zhao, C. M. et al. Denervation suppresses gastric tumorigenesis. Sci. Transl. Med. 6, 250ra115 (2014).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Magnon, C. et al. Autonomic nerve development contributes to prostate cancer progression. Science 341, 1236361 (2013).

    Article 
    PubMed 

    Google Scholar     

  • Balood, M. et al. Nociceptor neurons affect cancer immunosurveillance. Nature 611, 405–412 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Globig, A. M. et al. The β1-adrenergic receptor links sympathetic nerves to T cell exhaustion. Nature 622, 383–392 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Gerendai, I. et al. Transneuronal labelling of nerve cells in the CNS of female rat from the mammary gland by viral tracing technique. Neuroscience 108, 103–118 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Hebb, C. & Linzell, J. L. Innervation of the mammary gland. A histochemical study in the rabbit. Histochem. J. 2, 491–505 (1970).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Tavora, B. et al. Tumoural activation of TLR3–SLIT2 axis in endothelium drives metastasis. Nature 586, 299–304 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Brose, K. et al. Slit proteins bind Robo receptors and have an evolutionarily conserved role in repulsive axon guidance. Cell 96, 795–806 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Nguyen-Ngoc, K. V. et al. ECM microenvironment regulates collective migration and local dissemination in normal and malignant mammary epithelium. Proc. Natl Acad. Sci. USA 109, E2595–E2604 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Aslakson, C. J. & Miller, F. R. Selective events in the metastatic process defined by analysis of the sequential dissemination of subpopulations of a mouse mammary tumor. Cancer Res. 52, 1399–1405 (1992).

    CAS 
    PubMed 

    Google Scholar     

  • Bujak, J. K., Kosmala, D., Szopa, I. M., Majchrzak, K. & Bednarczyk, P. Inflammation, cancer and immunity-implication of TRPV1 channel. Front. Oncol. 9, 1087 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Liebig, C., Ayala, G., Wilks, J. A., Berger, D. H. & Albo, D. Perineural invasion in cancer: a review of the literature. Cancer 115, 3379–3391 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Kastin, A. Handbook of Biologically Active Peptides (Academic, 2013).

  • Otsuka, M. & Konishi, S. Release of substance P-like immunoreactivity from isolated spinal cord of newborn rat. Nature 264, 83–84 (1976).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Eshete, F. & Fields, R. D. Spike frequency decoding and autonomous activation of Ca2+-calmodulin-dependent protein kinase II in dorsal root ganglion neurons. J. Neurosci. 21, 6694–6705 (2001).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Nishikawa, S. et al. Two histidine residues are essential for ribonuclease T1 activity as is the case for ribonuclease A. Biochemistry 26, 8620–8624 (1987).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Robertson, H. D., Webster, R. E. & Zinder, N. D. Purification and properties of ribonuclease III from Escherichia coli. J. Biol. Chem. 243, 82–91 (1968).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Bremnes, R. M., Sirera, R. & Camps, C. Circulating tumour-derived DNA and RNA markers in blood: a tool for early detection, diagnostics, and follow-up? Lung Cancer 49, 1–12 (2005).

    Article 
    PubMed 

    Google Scholar     

  • Huang, W. et al. Site-specific RNase A activity was dramatically reduced in serum from multiple types of cancer patients. PLoS ONE 9, e96490 (2014).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • De Lamirande, G. Action of deoxyribonuclease and ribonuclease on the growth of Ehrlich ascites carcinoma in mice. Nature 192, 52–54 (1961).

    Article 
    ADS 
    PubMed 

    Google Scholar     

  • Ledoux, L. Action of ribonuclease on two solid tumours in vivo. Nature 176, 36–37 (1955).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Lund, J. M. et al. Recognition of single-stranded RNA viruses by Toll-like receptor 7. Proc. Natl Acad. Sci. USA 101, 5598–5603 (2004).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Kawasaki, T. & Kawai, T. Toll-like receptor signaling pathways. Front. Immunol. 5, 461 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Ojaniemi, M. et al. Phosphatidylinositol 3-kinase is involved in Toll-like receptor 4-mediated cytokine expression in mouse macrophages. Eur. J. Immunol. 33, 597–605 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Ha, T. et al. TLR2 ligands induce cardioprotection against ischaemia/reperfusion injury through a PI3K/Akt-dependent mechanism. Cardiovasc. Res. 87, 694–703 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Hesketh, P. J. et al. The oral neurokinin-1 antagonist aprepitant for the prevention of chemotherapy-induced nausea and vomiting: a multinational, randomized, double-blind, placebo-controlled trial in patients receiving high-dose cisplatin-the Aprepitant Protocol 052 Study Group. J. Clin. Oncol. 21, 4112–4119 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Rosso, M., Robles-Frias, M. J., Covenas, R., Salinas-Martin, M. V. & Munoz, M. The NK-1 receptor is expressed in human primary gastric and colon adenocarcinomas and is involved in the antitumor action of L-733,060 and the mitogenic action of substance P on human gastrointestinal cancer cell lines. Tumour Biol. 29, 245–254 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Munoz, M. & Rosso, M. The NK-1 receptor antagonist aprepitant as a broad spectrum antitumor drug. Invest. N. Drugs 28, 187–193 (2010).

    Article 
    CAS 

    Google Scholar     

  • Nagakawa, O. et al. Effect of prostatic neuropeptides on invasion and migration of PC-3 prostate cancer cells. Cancer Lett. 133, 27–33 (1998).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Nizam, E. & Erin, N. Differential consequences of neurokinin receptor 1 and 2 antagonists in metastatic breast carcinoma cells; effects independent of substance P. Biomed. Pharmacother. 108, 263–270 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Le, T. T. et al. Sensory nerves enhance triple-negative breast cancer invasion and metastasis via the axon guidance molecule PlexinB3. NPJ Breast Cancer 8, 116 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Austin, M., Elliott, L., Nicolaou, N., Grabowska, A. & Hulse, R. P. Breast cancer induced nociceptor aberrant growth and collateral sensory axonal branching. Oncotarget 8, 76606–76621 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Jurcak, N. R. et al. Axon guidance molecules promote perineural invasion and metastasis of orthotopic pancreatic tumors in mice. Gastroenterology 157, 838–850 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Kamiya, A. et al. Genetic manipulation of autonomic nerve fiber innervation and activity and its effect on breast cancer progression. Nat. Neurosci. 22, 1289–1305 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Zahalka, A. H. et al. Adrenergic nerves activate an angio-metabolic switch in prostate cancer. Science 358, 321–326 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Partecke, L. I. et al. Subdiaphragmatic vagotomy promotes tumor growth and reduces survival via TNFα in a murine pancreatic cancer model. Oncotarget 8, 22501–22512 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Renz, B. W. et al. Cholinergic signaling via muscarinic receptors directly and indirectly suppresses pancreatic tumorigenesis and cancer stemness. Cancer Discov. 8, 1458–1473 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Boilly, B., Faulkner, S., Jobling, P. & Hondermarck, H. Nerve dependence: from regeneration to cancer. Cancer Cell 31, 342–354 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Amit, M. et al. Loss of p53 drives neuron reprogramming in head and neck cancer. Nature 578, 449–454 (2020).

  • Kalinichenko, V. V., Mokyr, M. B., Graf, L. H. Jr, Cohen, R. L. & Chambers, D. A. Norepinephrine-mediated inhibition of antitumor cytotoxic T lymphocyte generation involves a β-adrenergic receptor mechanism and decreased TNF-α gene expression. J. Immunol. 163, 2492–2499 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Mohammadpour, H. et al. β2 adrenergic receptor-mediated signaling regulates the immunosuppressive potential of myeloid-derived suppressor cells. J. Clin. Invest. 129, 5537–5552 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Benci, J. L. et al. Opposing functions of interferon coordinate adaptive and innate immune responses to cancer immune checkpoint blockade. Cell 178, 933–948 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Cao, Y. Q. et al. Primary afferent tachykinins are required to experience moderate to intense pain. Nature 392, 390–394 (1998).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Wang, Y. et al. Ephrin-B2 controls VEGF-induced angiogenesis and lymphangiogenesis. Nature 465, 483–486 (2010).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Guy, C. T., Cardiff, R. D. & Muller, W. J. Induction of mammary tumors by expression of polyomavirus middle T oncogene: a transgenic mouse model for metastatic disease. Mol. Cell. Biol. 12, 954–961 (1992).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Maroulakou, I. G., Anver, M., Garrett, L. & Green, J. E. Prostate and mammary adenocarcinoma in transgenic mice carrying a rat C3(1) simian virus 40 large tumor antigen fusion gene. Proc. Natl Acad. Sci. USA 91, 11236–11240 (1994).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Hale, J. J. et al. Structural optimization affording 2-(R)-(1-(R)-3, 5-bis(trifluoromethyl)phenylethoxy)-3-(S)-(4-fluoro)phenyl-4- (3-oxo-1,2,4-triazol-5-yl)methylmorpholine, a potent, orally active, long-acting morpholine acetal human NK-1 receptor antagonist. J. Med. Chem. 41, 4607–4614 (1998).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Padmanaban, V. et al. Organotypic culture assays for murine and human primary and metastatic-site tumors. Nat. Protoc. 15, 2413–2442 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Young, L., Sung, J., Stacey, G. & Masters, J. R. Detection of Mycoplasma in cell cultures. Nat. Protoc. 5, 929–934 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Foty, R. A simple hanging drop cell culture protocol for generation of 3D spheroids. J. Vis. Exp. https://doi.org/10.3791/2720 (2011).

  • Heil, F. et al. Species-specific recognition of single-stranded RNA via Toll-like receptor 7 and 8. Science 303, 1526–1529 (2004).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Alexopoulou, L., Holt, A. C., Medzhitov, R. & Flavell, R. A. Recognition of double-stranded RNA and activation of NF-κB by Toll-like receptor 3. Nature 413, 732–738 (2001).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Hemmi, H. et al. Small anti-viral compounds activate immune cells via the TLR7 MyD88-dependent signaling pathway. Nat. Immunol. 3, 196–200 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Jurk, M. et al. Human TLR7 or TLR8 independently confer responsiveness to the antiviral compound R-848. Nat. Immunol. 3, 499 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Maira, S. M. et al. Identification and characterization of NVP-BKM120, an orally available pan-class I PI3-kinase inhibitor. Mol. Cancer Ther. 11, 317–328 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Davies, B. R. et al. Preclinical pharmacology of AZD5363, an inhibitor of AKT: pharmacodynamics, antitumor activity, and correlation of monotherapy activity with genetic background. Mol. Cancer Ther. 11, 873–887 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Folkes, A. J. et al. The identification of 2-(1H-indazol-4-yl)-6-(4-methanesulfonyl-piperazin-1-ylmethyl)-4-morpholin-4-yl-thieno[3,2-d]pyrimidine (GDC-0941) as a potent, selective, orally bioavailable inhibitor of class I PI3 kinase for the treatment of cancer. J. Med. Chem. 51, 5522–5532 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Padmanaban, V. et al. E-cadherin is required for metastasis in multiple models of breast cancer. Nature 573, 439–444 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

  • Krause, S., Brock, A. & Ingber, D. E. Intraductal injection for localized drug delivery to the mouse mammary gland. J. Vis. Exp. https://doi.org/10.3791/50692 (2013).

  • Jancso, G., Kiraly, E., Such, G., Joo, F. & Nagy, A. Neurotoxic effect of capsaicin in mammals. Acta Physiol. Hung. 69, 295–313 (1987).

    CAS 
    PubMed 

    Google Scholar     

  • Chi, J. et al. Three-dimensional adipose tissue imaging reveals regional variation in beige fat biogenesis and PRDM16-dependent sympathetic neurite density. Cell Metab. 27, 226–236 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Luppi, P. H., Fort, P. & Jouvet, M. Iontophoretic application of unconjugated cholera toxin B subunit (CTb) combined with immunohistochemistry of neurochemical substances: a method for transmitter identification of retrogradely labeled neurons. Brain Res. 534, 209–224 (1990).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Gee, K. R. et al. Chemical and physiological characterization of fluo-4 Ca2+-indicator dyes. Cell Calcium 27, 97–106 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar     

  • Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar     

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  • Cancer history raises cardiovascular disease risk in hypertensive patients

    Cancer history raises cardiovascular disease risk in hypertensive patients

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    A study published in the journal Hypertension Research reveals that having a cancer history can increase the risk of cardiovascular disease in individuals with hypertension.

    Study: Risk of cancer history in cardiovascular disease among individuals with hypertension. Image Credit: Black Salmon / ShutterstockStudy: Risk of cancer history in cardiovascular disease among individuals with hypertension. Image Credit: Black Salmon / Shutterstock

    Background

    A growing pool of evidence highlights the link between hypertension and cancer since both hypertension and cancer risks increase with advancing age, and that certain anti-cancer medications increase the risk of hypertension.

    Several epidemiological studies have found that hypertension can increase the risk of certain types of cancers and that individuals with a cancer history are more likely to develop cardiovascular complications.

    Given the potential link between the risks of hypertension, cardiovascular disease, and cancer, the scientists in this study have assessed the risk of cardiovascular disease events in hypertensive individuals with a history of cancer.

    Study design

    The study population included 747,620 individuals who were diagnosed with hypertension between January 2005 and May 2022. Patient information was collected from the JMDC Claims Database, a nationwide healthcare database in Japan.

    Appropriate statistical analyses were conducted to determine the risk of Composite cardiovascular disease events, including myocardial infarction, angina pectoris, stroke, heart failure, and atrial fibrillation, based on the participant’s history of cancer and chemotherapy.

    A history of cancer was defined as being diagnosed with malignancies before the initial health check-up.

    Self-reported information on comorbidities (obesity, diabetes, and dyslipidemia), alcohol intake and smoking status, and physical activity level was collected from participants during the health check-up.  

    Important observations

    A total of 26,531 individuals with a history of cancer were identified from the entire study population of 747,620 participants with hypertension. Participants with a history of cancer were more likely to be older adults, less likely to be men, and more likely to have diabetes. In contrast, participants without a history of cancer were more likely to have obesity and current smoking status.

    A total of 67,154 composite cardiovascular disease events were detected during the study follow-up period until May 2022. Hypertensive patients with a history of cancer showed a significantly higher risk of developing composite cardiovascular disease events. However, the risk of developing myocardial infarction was not statistically significant.

    The highest risk of developing cardiovascular disease events except myocardial infarction was observed among cancer survivors who received chemotherapy compared to those who did not receive chemotherapy or those without a history of cancer.

    Regarding myocardial infarction, a higher risk was observed among cancer patients receiving chemotherapy compared to those without a history of cancer.

    Five types of cancers, including colorectal cancer, prostate cancer, stomach cancer, renal, pelvic, and ureteral cancer, and lung cancer, showed the highest prevalence in men. In women, the highest prevalence was observed for breast cancer, colorectal cancer, thyroid cancer, corpus uteri cancer, and cervix uteri cancer. 

    A significantly higher risk of composite cardiovascular events was observed among men with a history of lung cancer and women with a history of breast cancer compared to those without a history of cancer.

    A sensitivity analysis conducted after adjusting for age, sex, smoking status, alcohol intake, and physical inactivity showed a similar positive association between having a cancer history and risk of composite cardiovascular disease events.

    Study significance

    The study finds that hypertensive patients with a history of cancer have a higher risk of developing various cardiovascular disease events than those without a history of cancer. The risk of cardiovascular disease events is even higher in cancer patients receiving chemotherapy.

    The study findings highlight the need for early screening of cancer in patients with hypertension. Physicians should manage hypertensive individuals more carefully as cancer comorbidity not only adversely affects cancer mortality but also significantly increases the risk of cardiovascular disease.   

    The coexistence of cancer and hypertension is a significant public health crisis in Asian countries. Implementation of appropriate healthcare policies is needed to prevent detrimental cardiovascular health consequences, particularly in developing countries with advanced aging.

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  • Breast cancer survivors at higher risk of developing second cancers

    Breast cancer survivors at higher risk of developing second cancers

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    Survivors of breast cancer are at significantly higher risk of developing second cancers, including endometrial and ovarian cancer for women and prostate cancer for men, according to new research studying data from almost 600,000 patients in England.

    For the first time, the research has shown that this risk is higher in people living in areas of greater socioeconomic deprivation.

    Breast cancer is the most commonly diagnosed cancer in the UK. Around 56,000 people in the UK are diagnosed each year, the vast majority (over 99%) of whom are women. Improvements in earlier diagnosis and in treatments mean that five year survival rates have been increasing over time, reaching 87% by 2017 in England.

    People who survive breast cancer are at risk of second primary cancer, but until now the exact risk has been unclear. Previously published research suggested that women and men who survive breast cancer are at a 24% and 27% greater risk of a non-breast second primary cancer than the wider population respectively. There have been also suggestions that second primary cancer risks differ by the age at breast cancer diagnosis.

    To provide more accurate estimates, a team led by researchers at the University of Cambridge analysed data from over 580,000 female and over 3,500 male breast cancer survivors diagnosed between 1995 and 2019 using the National Cancer Registration Dataset. The results of their analysis are published today in Lancet Regional Health – Europe.

    It’s important for us to understand to what extent having one type of cancer puts you at risk of a second cancer at a different site. The female and male breast cancer survivors whose data we studied were at increased risk of a number of second cancers. Knowing this can help inform conversations with their care teams to look out for signs of potential new cancers.”


    Isaac Allen, First Author, Department of Public Health and Primary Care, University of Cambridge

    The researchers found significantly increased risks of cancer in the contralateral (that is, unaffected) breast and for endometrium and prostate cancer in females and males, respectively. Females who survived breast cancer were at double the risk of contralateral breast cancer compared to the general population and at 87% greater risk of endometrial cancer, 58% greater risk of myeloid leukemia and 25% greater risk of ovarian cancer.

    Age of diagnosis was important, too – females diagnosed with breast cancer under the age of 50 were 86% more likely to develop a second primary cancer compared to the general population of the same age, whereas women diagnosed after age 50 were at a 17% increased risk. One potential explanation is that a larger number of younger breast cancer survivors may have inherited genetic alterations that increase risk for multiple cancers. For example, women with inherited changes to the BRCA1 and BRCA2 genes are at increased risk of contralateral breast cancer, ovarian and pancreatic cancer.

    Females from the most socioeconomically deprived backgrounds were at 35% greater risk of a second primary cancer compared to females from the least deprived backgrounds. These differences were primarily driven by non-breast cancer risks, particularly for lung, kidney, head and neck, bladder, esophageal and stomach cancers. This may be because smoking, obesity, and alcohol consumption – established risk factors for these cancers – are more common among more deprived groups.

    Allen, a PhD student at Clare Hall, added: “This is further evidence of the health inequalities that people from more deprived backgrounds experience. We need to fully understand why they are at greater risk of second cancers so that we can intervene and reduce this risk.”

    Male breast cancer survivors were 55 times more likely than the general male population to develop contralateral breast cancer – though the researchers stress that an individual’s risk was still very low. For example, for every 100 men diagnosed with breast cancer at age 50 or over, about three developed contralateral breast cancer during a 25 year period. Male breast cancer survivors were also 58% more likely than the general male population to develop prostate cancer.

    Professor Antonis Antoniou from the Department of Public Health and Primary Care at the University of Cambridge, the study’s senior author, said: “This is the largest study to date to look at the risk in breast cancer survivors of developing a second cancer. We were able to carry this out and calculate more accurate estimates because of the outstanding data sets available to researchers through the NHS.”

    The research was funded by Cancer Research UK with support from the National Institute for Health and Care Research Cambridge Biomedical Research Centre.

    Cancer Research UK’s senior cancer intelligence manager, Katrina Brown, said: “This study shows us that the risk of second primary cancers is higher in people who have had breast cancer, and this can differ depending on someone’s socioeconomic background. But more research is needed to understand what is driving this difference and how to tackle these health inequalities.”

    People who are concerned about their cancer risk should contact their GP for advice. If you or someone close to you have been affected by cancer and you’ve got questions, you can call Cancer Research UK nurses on freephone 0808 800 4040, Monday to Friday.

    Source:

    Journal reference:

    Allen, I., et al. (2024) Risks of second primary cancers among 584,965 female and male breast cancer survivors in England: a 25-year retrospective cohort study. The Lancet Regional Health – Europe. doi.org/10.1016/j.lanepe.2024.100903.

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  • Do neighborhood-level disparities in breast cancer–specific survival remain after accounting for individual, tumor, and treatment characteristics?

    Do neighborhood-level disparities in breast cancer–specific survival remain after accounting for individual, tumor, and treatment characteristics?

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    The screening, treatment, and survival in breast cancer cases have improved considerably, but differences in survival outcomes persist, especially in disadvantaged neighborhoods.

    A recent JAMA Network Open study assessed the role of neighborhood-level disparities in determining shorter breast cancer-specific survival after accounting for several confounding factors related to the tumor, individual, and treatment characteristics.

    Study: Neighborhood Disadvantage and Breast Cancer–Specific Survival in the US. Image Credit: Gorodenkoff/Shutterstock.comStudy: Neighborhood Disadvantage and Breast Cancer–Specific Survival in the US. Image Credit: Gorodenkoff/Shutterstock.com

    Background

    Neighborhoods are complex environments with distinct physical, cultural, and economic attributes. Communities facing economic challenges may have limited access to essential health services, including facilities for screening, mammography, and treatment. This lack of resources can affect breast cancer survival rates in these areas.

    Research has shown the association between neighborhood disadvantage and breast cancer survival, even after controlling for many of the neighborhood-level, individual-level, and structural factors.

    This suggests the presence of some unaccounted underlying mechanisms which have not been explored yet. Most prior research has focussed on specific geographic areas or subsets of the population, which has limited its generalizability.

    About this study

    This study used a robust index of neighborhood disadvantage (Yost index) to assess the association between neighborhood disadvantage and breast cancer-specific and overall survival post-controlling for tumor, individual, and treatment factors in a national cohort. The null hypothesis was the presence of survival disparities post-adjustment for confounders.

    The cohort comprised patients who were diagnosed with breast cancer from 2013 to 2018 and were sourced from the data held by the National Cancer Institute. The main exposure measure was the neighborhood disadvantage, captured by Yost index quintiles. The main outcome variable was breast cancer-specific survival.

    This was measured using a cause-specific hazard model controlling. Several factors were controlled for, including race, stage, rurality, age, ethnicity, insurance, subtype, and receipt of treatment.

    Study findings

    It was noted that patients in the most disadvantaged neighborhoods reported shorter breast cancer-specific and overall survival rates. This association prevailed even after adjusting for treatment characteristics that are commonly used to explain survival differences.

    This suggests the presence of unaccounted-for non-biological or biological mechanisms through which breast cancer-specific survival is influenced by neighborhood disadvantage.

    The findings raise an important question regarding whether neighborhood disadvantage could give rise to more malignant tumor biology and low survival rates.

    In social genomics studies, psychological and biological stress has been associated with neighborhood disadvantage. These stresses lead to upregulating stress-related neuroendocrine signaling pathways by placing demands on the sympathetic nervous system.

    The signaling pathways lead to a gene expression called conserved transcriptional response to adversity (CTRA). Research has associated the CTRA gene expression with upregulation of proinflammatory gene expression.

    This, in turn, facilitates a prometastatic environment, suggesting aggressive tumor biology. Therefore, this is how breast cancer survival could be affected by neighborhood disadvantage. It must be noted that more research is needed to closely examine the association between the CTRA gene, tumor expression, and neighborhood disadvantage in humans.

    Non-Hispanic Black patients were seen to have the highest mortality risk relative to non-Hispanic White and Hispanic patients. This is indicative of the fact that social determinants of health were not fully controlled for.

    For the relative risk of triple-negative breast cancer (TNBC), similar results were noted when contrasted with human epidermal growth factor receptor two negative (ERBB2−) disease.

    It was also mentioned that unaccounted-for non-biologic pathways could also be driving the persistent disparities in breast cancer-specific survival.

    The immeasurable effects of structural racism, financial burden, and trust on healthcare practitioners could all drive inequities in survival. Future research should appropriately account for these factors.

    Conclusions

    In sum, neighborhood disadvantage was noted to be independently associated with shorter survival in breast cancer patients despite accounting for tumor characteristics, individual-level factors, and treatment.

    In order to understand these residual disparities better, future research should consider the components of the built environment, which could influence outcomes.

    In this regard, a translational epidemiologic approach could be the way forward. This could consider neighborhood disadvantages when devising cancer control interventions and risk-stratifying vulnerable populations.

    In this way, the translational epidemiologic approach could advance precision medicine in oncology.

    Similar to previous retrospective observational studies, this study also has certain limitations. Information on comorbidities, medicine doses, treatment rationale, and completion of radiotherapy and chemotherapy could not be obtained.

    Another limitation concerns the important issue of access to care. Individual insurance coverage was used as a proxy for access to care; however, this is an incomplete representation of all access to care measures.

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  • Korean fermented food Doenjang shows promise in alleviating menopausal symptoms

    Korean fermented food Doenjang shows promise in alleviating menopausal symptoms

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    In a recent study published in the journal Nutrients, researchers compare the efficacy between traditional and commercial Doenjang in alleviating menopausal syndrome.

    Study: Evaluation of menopausal syndrome relief and anti-obesity efficacy of the Korean fermented food doenjang: a randomized, double-blind clinical trial. Image Credit: mino choi / Shutterstock.com

    Treating menopause

    Menopause leads to hormonal changes that may cause psychological and physical symptoms like hot flashes, sleep disturbances, insomnia, sweating, atrophy of the genitourinary system, and anxiety. Menopausal symptoms are often treated with hormone replacement therapy (HRT), primarily progesterone and estrogen.

    Soy consumption, especially in Asian countries, is associated with lower rates of menopausal symptoms, thereby offering a natural substitute for HRT when it may be contraindicated or otherwise undesirable due to possible side effects such as breast cancer or cardiovascular disease.

    Doenjang, a form of soybean paste consumed in Korea, is rich in antioxidants and fermented with beneficial microorganisms like Bacillus subtilis, Aspergillus oryzae, Rhizopus, and Mucor. Previously, Doenjang has been identified as a potential solution for alleviating menopausal symptoms; however, careful evaluation is needed to identify formulations that may be most effective for this purpose.

    About the study

    In the current study, researchers conducted an eight-week randomized and double-blind clinical trial involving postmenopausal women with menopausal syndrome.

    The Kupperman index is a widely accepted diagnostic tool for menopausal syndrome that measures a wide range of vasomotor, urinary tract, psychoneurological, motor, digestive, and systemic symptoms. Scores below 20 indicate mild symptoms, while those between 20 and 40 signify moderate severity. Scores exceeding 40 indicate severe symptoms and scores of 60 or more denote a particularly severe manifestation of the syndrome.

    Participants received Doenjang supplementation in three forms, including high-content beneficial microorganism traditional Doenjang (HDC), low-content beneficial microorganism traditional Doenjang (LDC), and commercially available Doenjang (CD).

    Medication compliance, vital signs, and side effects were evaluated after four weeks of supplementation. Researchers collected measures of menopausal syndrome relief, including the Kupperman index, at the beginning and end of the trial, along with bioelectrical impedance analysis (BIA) results, weight, lipid profiles, serum blood markers, and inflammatory markers.

    Safety assessments included blood chemistry, hematological tests, and monitoring for side effects. Obesity indicators and inflammation markers were also assessed, as were changes in the gut microbiome analyzed through stool tests.

    Doenjang pills were prepared through a traditional fermentation process and freeze-dried for clinical trials. Statistical analysis included chi-square tests, analysis of variance (ANOVA), and paired t-tests to compare baseline and post-intervention data.

    Study findings

    A total of 56 individuals were included in the study and received HDC, LDC, or CD, none of whom reported any adverse events. Anthropometric parameters, including age, weight, and body mass index (BMI), did not differ significantly among the study participants.

    Safety assessments indicated no adverse effects on liver or kidney function, with some improvements in blood urea nitrogen (BUN), uric acid, and total protein levels in the HDC group. Doenjang was not associated with anti-obesity effects; however, its use reduced LDL cholesterol levels.

    Kupperman index scores significantly decreased in all groups following the administration of Doenjang, with improvements observed in various symptoms. Microbiome analysis showed decreased Firmicutes and increased Bacteroidetes across all groups, with beneficial bacteria increasing and harmful bacteria decreasing, particularly in the CD group.

    Short-chain fatty acid analysis indicated varied effects across groups. Overall, while Doenjang showed promise in alleviating menopausal symptoms and modifying gut microbiota, its effects on obesity and inflammation were limited.

    Conclusions

    Traditional Doenjang fermented with beneficial microorganisms was found to be superior in its ability to mitigate menopausal symptoms as compared to commercial Doenjang. Nevertheless, a significant reduction in Kupperman index scores was observed across all groups, with the most notable improvement observed in LDC recipients.

    LDL cholesterol levels decreased in both traditional Doenjang groups, thus indicating its potential cardiovascular benefits. Although Doenjang was not associated with anti-obesity or anti-inflammatory effects, its treatment positively influenced gut microbiota by increasing beneficial bacteria and reducing the levels of harmful bacteria.

    Taken together, these findings demonstrate that traditional Doenjang has the potential to effectively alleviate menopausal symptoms, particularly when considering cardiovascular health, with implications for improving intestinal health through its impact on gut microbiota.

    Some limitations of the current study include the inability to control participants’ lifestyle factors, the short study duration, and the small sample size. Thus, future studies with larger samples and longer durations to elucidate the therapeutic potential of Doenjang.

    Journal reference:

    • Han, A.L., Ryu, M.S., Yang, H., et al. (2024). Evaluation of menopausal syndrome relief and anti-obesity efficacy of the Korean fermented food doenjang: a randomized, double-blind clinical trial. Nutrients. doi:10.3390/nu16081194

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